Overview

Dataset statistics

Number of variables366
Number of observations891221
Missing cells33492923
Missing cells (%)10.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 GiB
Average record size in memory2.9 KiB

Variable types

Numeric208
Categorical158

Alerts

EINGEFUEGT_AM has a high cardinality: 5162 distinct values High cardinality
AKT_DAT_KL has 73499 (8.2%) missing values Missing
ALTER_HH has 73499 (8.2%) missing values Missing
ALTER_KIND1 has 810163 (90.9%) missing values Missing
ALTER_KIND2 has 861722 (96.7%) missing values Missing
ALTER_KIND3 has 885051 (99.3%) missing values Missing
ALTER_KIND4 has 890016 (99.9%) missing values Missing
ALTERSKATEGORIE_FEIN has 262947 (29.5%) missing values Missing
ANZ_HAUSHALTE_AKTIV has 93148 (10.5%) missing values Missing
ANZ_HH_TITEL has 97008 (10.9%) missing values Missing
ANZ_KINDER has 73499 (8.2%) missing values Missing
ANZ_PERSONEN has 73499 (8.2%) missing values Missing
ANZ_STATISTISCHE_HAUSHALTE has 93148 (10.5%) missing values Missing
ANZ_TITEL has 73499 (8.2%) missing values Missing
ARBEIT has 97216 (10.9%) missing values Missing
BALLRAUM has 93740 (10.5%) missing values Missing
CAMEO_DEU_2015 has 98979 (11.1%) missing values Missing
CAMEO_DEUG_2015 has 98979 (11.1%) missing values Missing
CAMEO_INTL_2015 has 98979 (11.1%) missing values Missing
D19_BANKEN_ONLINE_QUOTE_12 has 257113 (28.8%) missing values Missing
D19_GESAMT_ONLINE_QUOTE_12 has 257113 (28.8%) missing values Missing
D19_KONSUMTYP has 257113 (28.8%) missing values Missing
D19_LETZTER_KAUF_BRANCHE has 257113 (28.8%) missing values Missing
D19_LOTTO has 257113 (28.8%) missing values Missing
D19_SOZIALES has 257113 (28.8%) missing values Missing
D19_TELKO_ONLINE_QUOTE_12 has 257113 (28.8%) missing values Missing
D19_VERSAND_ONLINE_QUOTE_12 has 257113 (28.8%) missing values Missing
D19_VERSI_ONLINE_QUOTE_12 has 257113 (28.8%) missing values Missing
DSL_FLAG has 93148 (10.5%) missing values Missing
EINGEFUEGT_AM has 93148 (10.5%) missing values Missing
EINGEZOGENAM_HH_JAHR has 73499 (8.2%) missing values Missing
EWDICHTE has 93740 (10.5%) missing values Missing
EXTSEL992 has 654153 (73.4%) missing values Missing
FIRMENDICHTE has 93155 (10.5%) missing values Missing
GEBAEUDETYP has 93148 (10.5%) missing values Missing
GEBAEUDETYP_RASTER has 93155 (10.5%) missing values Missing
GEMEINDETYP has 97274 (10.9%) missing values Missing
HH_DELTA_FLAG has 107602 (12.1%) missing values Missing
HH_EINKOMMEN_SCORE has 18348 (2.1%) missing values Missing
INNENSTADT has 93740 (10.5%) missing values Missing
KBA05_ALTER1 has 133324 (15.0%) missing values Missing
KBA05_ALTER2 has 133324 (15.0%) missing values Missing
KBA05_ALTER3 has 133324 (15.0%) missing values Missing
KBA05_ALTER4 has 133324 (15.0%) missing values Missing
KBA05_ANHANG has 133324 (15.0%) missing values Missing
KBA05_ANTG1 has 133324 (15.0%) missing values Missing
KBA05_ANTG2 has 133324 (15.0%) missing values Missing
KBA05_ANTG3 has 133324 (15.0%) missing values Missing
KBA05_ANTG4 has 133324 (15.0%) missing values Missing
KBA05_AUTOQUOT has 133324 (15.0%) missing values Missing
KBA05_BAUMAX has 133324 (15.0%) missing values Missing
KBA05_CCM1 has 133324 (15.0%) missing values Missing
KBA05_CCM2 has 133324 (15.0%) missing values Missing
KBA05_CCM3 has 133324 (15.0%) missing values Missing
KBA05_CCM4 has 133324 (15.0%) missing values Missing
KBA05_DIESEL has 133324 (15.0%) missing values Missing
KBA05_FRAU has 133324 (15.0%) missing values Missing
KBA05_GBZ has 133324 (15.0%) missing values Missing
KBA05_HERST1 has 133324 (15.0%) missing values Missing
KBA05_HERST2 has 133324 (15.0%) missing values Missing
KBA05_HERST3 has 133324 (15.0%) missing values Missing
KBA05_HERST4 has 133324 (15.0%) missing values Missing
KBA05_HERST5 has 133324 (15.0%) missing values Missing
KBA05_HERSTTEMP has 93148 (10.5%) missing values Missing
KBA05_KRSAQUOT has 133324 (15.0%) missing values Missing
KBA05_KRSHERST1 has 133324 (15.0%) missing values Missing
KBA05_KRSHERST2 has 133324 (15.0%) missing values Missing
KBA05_KRSHERST3 has 133324 (15.0%) missing values Missing
KBA05_KRSKLEIN has 133324 (15.0%) missing values Missing
KBA05_KRSOBER has 133324 (15.0%) missing values Missing
KBA05_KRSVAN has 133324 (15.0%) missing values Missing
KBA05_KRSZUL has 133324 (15.0%) missing values Missing
KBA05_KW1 has 133324 (15.0%) missing values Missing
KBA05_KW2 has 133324 (15.0%) missing values Missing
KBA05_KW3 has 133324 (15.0%) missing values Missing
KBA05_MAXAH has 133324 (15.0%) missing values Missing
KBA05_MAXBJ has 133324 (15.0%) missing values Missing
KBA05_MAXHERST has 133324 (15.0%) missing values Missing
KBA05_MAXSEG has 133324 (15.0%) missing values Missing
KBA05_MAXVORB has 133324 (15.0%) missing values Missing
KBA05_MOD1 has 133324 (15.0%) missing values Missing
KBA05_MOD2 has 133324 (15.0%) missing values Missing
KBA05_MOD3 has 133324 (15.0%) missing values Missing
KBA05_MOD4 has 133324 (15.0%) missing values Missing
KBA05_MOD8 has 133324 (15.0%) missing values Missing
KBA05_MODTEMP has 93148 (10.5%) missing values Missing
KBA05_MOTOR has 133324 (15.0%) missing values Missing
KBA05_MOTRAD has 133324 (15.0%) missing values Missing
KBA05_SEG1 has 133324 (15.0%) missing values Missing
KBA05_SEG10 has 133324 (15.0%) missing values Missing
KBA05_SEG2 has 133324 (15.0%) missing values Missing
KBA05_SEG3 has 133324 (15.0%) missing values Missing
KBA05_SEG4 has 133324 (15.0%) missing values Missing
KBA05_SEG5 has 133324 (15.0%) missing values Missing
KBA05_SEG6 has 133324 (15.0%) missing values Missing
KBA05_SEG7 has 133324 (15.0%) missing values Missing
KBA05_SEG8 has 133324 (15.0%) missing values Missing
KBA05_SEG9 has 133324 (15.0%) missing values Missing
KBA05_VORB0 has 133324 (15.0%) missing values Missing
KBA05_VORB1 has 133324 (15.0%) missing values Missing
KBA05_VORB2 has 133324 (15.0%) missing values Missing
KBA05_ZUL1 has 133324 (15.0%) missing values Missing
KBA05_ZUL2 has 133324 (15.0%) missing values Missing
KBA05_ZUL3 has 133324 (15.0%) missing values Missing
KBA05_ZUL4 has 133324 (15.0%) missing values Missing
KBA13_ALTERHALTER_30 has 105800 (11.9%) missing values Missing
KBA13_ALTERHALTER_45 has 105800 (11.9%) missing values Missing
KBA13_ALTERHALTER_60 has 105800 (11.9%) missing values Missing
KBA13_ALTERHALTER_61 has 105800 (11.9%) missing values Missing
KBA13_ANTG1 has 105800 (11.9%) missing values Missing
KBA13_ANTG2 has 105800 (11.9%) missing values Missing
KBA13_ANTG3 has 105800 (11.9%) missing values Missing
KBA13_ANTG4 has 105800 (11.9%) missing values Missing
KBA13_ANZAHL_PKW has 105800 (11.9%) missing values Missing
KBA13_AUDI has 105800 (11.9%) missing values Missing
KBA13_AUTOQUOTE has 105800 (11.9%) missing values Missing
KBA13_BAUMAX has 105800 (11.9%) missing values Missing
KBA13_BJ_1999 has 105800 (11.9%) missing values Missing
KBA13_BJ_2000 has 105800 (11.9%) missing values Missing
KBA13_BJ_2004 has 105800 (11.9%) missing values Missing
KBA13_BJ_2006 has 105800 (11.9%) missing values Missing
KBA13_BJ_2008 has 105800 (11.9%) missing values Missing
KBA13_BJ_2009 has 105800 (11.9%) missing values Missing
KBA13_BMW has 105800 (11.9%) missing values Missing
KBA13_CCM_0_1400 has 105800 (11.9%) missing values Missing
KBA13_CCM_1000 has 105800 (11.9%) missing values Missing
KBA13_CCM_1200 has 105800 (11.9%) missing values Missing
KBA13_CCM_1400 has 105800 (11.9%) missing values Missing
KBA13_CCM_1401_2500 has 105800 (11.9%) missing values Missing
KBA13_CCM_1500 has 105800 (11.9%) missing values Missing
KBA13_CCM_1600 has 105800 (11.9%) missing values Missing
KBA13_CCM_1800 has 105800 (11.9%) missing values Missing
KBA13_CCM_2000 has 105800 (11.9%) missing values Missing
KBA13_CCM_2500 has 105800 (11.9%) missing values Missing
KBA13_CCM_2501 has 105800 (11.9%) missing values Missing
KBA13_CCM_3000 has 105800 (11.9%) missing values Missing
KBA13_CCM_3001 has 105800 (11.9%) missing values Missing
KBA13_FAB_ASIEN has 105800 (11.9%) missing values Missing
KBA13_FAB_SONSTIGE has 105800 (11.9%) missing values Missing
KBA13_FIAT has 105800 (11.9%) missing values Missing
KBA13_FORD has 105800 (11.9%) missing values Missing
KBA13_GBZ has 105800 (11.9%) missing values Missing
KBA13_HALTER_20 has 105800 (11.9%) missing values Missing
KBA13_HALTER_25 has 105800 (11.9%) missing values Missing
KBA13_HALTER_30 has 105800 (11.9%) missing values Missing
KBA13_HALTER_35 has 105800 (11.9%) missing values Missing
KBA13_HALTER_40 has 105800 (11.9%) missing values Missing
KBA13_HALTER_45 has 105800 (11.9%) missing values Missing
KBA13_HALTER_50 has 105800 (11.9%) missing values Missing
KBA13_HALTER_55 has 105800 (11.9%) missing values Missing
KBA13_HALTER_60 has 105800 (11.9%) missing values Missing
KBA13_HALTER_65 has 105800 (11.9%) missing values Missing
KBA13_HALTER_66 has 105800 (11.9%) missing values Missing
KBA13_HERST_ASIEN has 105800 (11.9%) missing values Missing
KBA13_HERST_AUDI_VW has 105800 (11.9%) missing values Missing
KBA13_HERST_BMW_BENZ has 105800 (11.9%) missing values Missing
KBA13_HERST_EUROPA has 105800 (11.9%) missing values Missing
KBA13_HERST_FORD_OPEL has 105800 (11.9%) missing values Missing
KBA13_HERST_SONST has 105800 (11.9%) missing values Missing
KBA13_HHZ has 105800 (11.9%) missing values Missing
KBA13_KMH_0_140 has 105800 (11.9%) missing values Missing
KBA13_KMH_110 has 105800 (11.9%) missing values Missing
KBA13_KMH_140 has 105800 (11.9%) missing values Missing
KBA13_KMH_140_210 has 105800 (11.9%) missing values Missing
KBA13_KMH_180 has 105800 (11.9%) missing values Missing
KBA13_KMH_210 has 105800 (11.9%) missing values Missing
KBA13_KMH_211 has 105800 (11.9%) missing values Missing
KBA13_KMH_250 has 105800 (11.9%) missing values Missing
KBA13_KMH_251 has 105800 (11.9%) missing values Missing
KBA13_KRSAQUOT has 105800 (11.9%) missing values Missing
KBA13_KRSHERST_AUDI_VW has 105800 (11.9%) missing values Missing
KBA13_KRSHERST_BMW_BENZ has 105800 (11.9%) missing values Missing
KBA13_KRSHERST_FORD_OPEL has 105800 (11.9%) missing values Missing
KBA13_KRSSEG_KLEIN has 105800 (11.9%) missing values Missing
KBA13_KRSSEG_OBER has 105800 (11.9%) missing values Missing
KBA13_KRSSEG_VAN has 105800 (11.9%) missing values Missing
KBA13_KRSZUL_NEU has 105800 (11.9%) missing values Missing
KBA13_KW_0_60 has 105800 (11.9%) missing values Missing
KBA13_KW_110 has 105800 (11.9%) missing values Missing
KBA13_KW_120 has 105800 (11.9%) missing values Missing
KBA13_KW_121 has 105800 (11.9%) missing values Missing
KBA13_KW_30 has 105800 (11.9%) missing values Missing
KBA13_KW_40 has 105800 (11.9%) missing values Missing
KBA13_KW_50 has 105800 (11.9%) missing values Missing
KBA13_KW_60 has 105800 (11.9%) missing values Missing
KBA13_KW_61_120 has 105800 (11.9%) missing values Missing
KBA13_KW_70 has 105800 (11.9%) missing values Missing
KBA13_KW_80 has 105800 (11.9%) missing values Missing
KBA13_KW_90 has 105800 (11.9%) missing values Missing
KBA13_MAZDA has 105800 (11.9%) missing values Missing
KBA13_MERCEDES has 105800 (11.9%) missing values Missing
KBA13_MOTOR has 105800 (11.9%) missing values Missing
KBA13_NISSAN has 105800 (11.9%) missing values Missing
KBA13_OPEL has 105800 (11.9%) missing values Missing
KBA13_PEUGEOT has 105800 (11.9%) missing values Missing
KBA13_RENAULT has 105800 (11.9%) missing values Missing
KBA13_SEG_GELAENDEWAGEN has 105800 (11.9%) missing values Missing
KBA13_SEG_GROSSRAUMVANS has 105800 (11.9%) missing values Missing
KBA13_SEG_KLEINST has 105800 (11.9%) missing values Missing
KBA13_SEG_KLEINWAGEN has 105800 (11.9%) missing values Missing
KBA13_SEG_KOMPAKTKLASSE has 105800 (11.9%) missing values Missing
KBA13_SEG_MINIVANS has 105800 (11.9%) missing values Missing
KBA13_SEG_MINIWAGEN has 105800 (11.9%) missing values Missing
KBA13_SEG_MITTELKLASSE has 105800 (11.9%) missing values Missing
KBA13_SEG_OBEREMITTELKLASSE has 105800 (11.9%) missing values Missing
KBA13_SEG_OBERKLASSE has 105800 (11.9%) missing values Missing
KBA13_SEG_SONSTIGE has 105800 (11.9%) missing values Missing
KBA13_SEG_SPORTWAGEN has 105800 (11.9%) missing values Missing
KBA13_SEG_UTILITIES has 105800 (11.9%) missing values Missing
KBA13_SEG_VAN has 105800 (11.9%) missing values Missing
KBA13_SEG_WOHNMOBILE has 105800 (11.9%) missing values Missing
KBA13_SITZE_4 has 105800 (11.9%) missing values Missing
KBA13_SITZE_5 has 105800 (11.9%) missing values Missing
KBA13_SITZE_6 has 105800 (11.9%) missing values Missing
KBA13_TOYOTA has 105800 (11.9%) missing values Missing
KBA13_VORB_0 has 105800 (11.9%) missing values Missing
KBA13_VORB_1 has 105800 (11.9%) missing values Missing
KBA13_VORB_1_2 has 105800 (11.9%) missing values Missing
KBA13_VORB_2 has 105800 (11.9%) missing values Missing
KBA13_VORB_3 has 105800 (11.9%) missing values Missing
KBA13_VW has 105800 (11.9%) missing values Missing
KK_KUNDENTYP has 584612 (65.6%) missing values Missing
KKK has 121196 (13.6%) missing values Missing
KONSUMNAEHE has 73969 (8.3%) missing values Missing
KONSUMZELLE has 93155 (10.5%) missing values Missing
MIN_GEBAEUDEJAHR has 93148 (10.5%) missing values Missing
MOBI_RASTER has 93148 (10.5%) missing values Missing
MOBI_REGIO has 133324 (15.0%) missing values Missing
ORTSGR_KLS9 has 97216 (10.9%) missing values Missing
OST_WEST_KZ has 93148 (10.5%) missing values Missing
PLZ8_ANTG1 has 116515 (13.1%) missing values Missing
PLZ8_ANTG2 has 116515 (13.1%) missing values Missing
PLZ8_ANTG3 has 116515 (13.1%) missing values Missing
PLZ8_ANTG4 has 116515 (13.1%) missing values Missing
PLZ8_BAUMAX has 116515 (13.1%) missing values Missing
PLZ8_GBZ has 116515 (13.1%) missing values Missing
PLZ8_HHZ has 116515 (13.1%) missing values Missing
REGIOTYP has 121196 (13.6%) missing values Missing
RELAT_AB has 97216 (10.9%) missing values Missing
RT_UEBERGROESSE has 51226 (5.7%) missing values Missing
SOHO_KZ has 73499 (8.2%) missing values Missing
STRUKTURTYP has 97274 (10.9%) missing values Missing
TITEL_KZ has 73499 (8.2%) missing values Missing
UMFELD_ALT has 97786 (11.0%) missing values Missing
UMFELD_JUNG has 97786 (11.0%) missing values Missing
UNGLEICHENN_FLAG has 73499 (8.2%) missing values Missing
VERDICHTUNGSRAUM has 97274 (10.9%) missing values Missing
VHA has 73499 (8.2%) missing values Missing
VHN has 121196 (13.6%) missing values Missing
VK_DHT4A has 75917 (8.5%) missing values Missing
VK_DISTANZ has 75917 (8.5%) missing values Missing
VK_ZG11 has 75917 (8.5%) missing values Missing
W_KEIT_KIND_HH has 107602 (12.1%) missing values Missing
WOHNDAUER_2008 has 73499 (8.2%) missing values Missing
WOHNLAGE has 93148 (10.5%) missing values Missing
ANZ_HH_TITEL is highly skewed (γ1 = 22.71869357) Skewed
TITEL_KZ is highly skewed (γ1 = 39.64777145) Skewed
LNR is uniformly distributed Uniform
LNR has unique values Unique
ALTER_HH has 236768 (26.6%) zeros Zeros
ALTERSKATEGORIE_FEIN has 41188 (4.6%) zeros Zeros
ANZ_HH_TITEL has 770244 (86.4%) zeros Zeros
ANZ_KINDER has 731242 (82.0%) zeros Zeros
ANZ_PERSONEN has 34103 (3.8%) zeros Zeros
ANZ_TITEL has 814542 (91.4%) zeros Zeros
D19_BANKEN_ANZ_12 has 831734 (93.3%) zeros Zeros
D19_BANKEN_ANZ_24 has 794100 (89.1%) zeros Zeros
D19_BANKEN_DIREKT has 728811 (81.8%) zeros Zeros
D19_BANKEN_GROSS has 785351 (88.1%) zeros Zeros
D19_BANKEN_LOKAL has 874745 (98.2%) zeros Zeros
D19_BANKEN_ONLINE_QUOTE_12 has 588874 (66.1%) zeros Zeros
D19_BANKEN_REST has 821760 (92.2%) zeros Zeros
D19_BEKLEIDUNG_GEH has 809304 (90.8%) zeros Zeros
D19_BEKLEIDUNG_REST has 692502 (77.7%) zeros Zeros
D19_BILDUNG has 813156 (91.2%) zeros Zeros
D19_BIO_OEKO has 854074 (95.8%) zeros Zeros
D19_BUCH_CD has 622788 (69.9%) zeros Zeros
D19_DIGIT_SERV has 857661 (96.2%) zeros Zeros
D19_DROGERIEARTIKEL has 761014 (85.4%) zeros Zeros
D19_ENERGIE has 829857 (93.1%) zeros Zeros
D19_FREIZEIT has 790748 (88.7%) zeros Zeros
D19_GARTEN has 851626 (95.6%) zeros Zeros
D19_GESAMT_ANZ_12 has 584797 (65.6%) zeros Zeros
D19_GESAMT_ANZ_24 has 505303 (56.7%) zeros Zeros
D19_GESAMT_ONLINE_QUOTE_12 has 393075 (44.1%) zeros Zeros
D19_HANDWERK has 768381 (86.2%) zeros Zeros
D19_HAUS_DEKO has 713100 (80.0%) zeros Zeros
D19_KINDERARTIKEL has 749365 (84.1%) zeros Zeros
D19_KOSMETIK has 745836 (83.7%) zeros Zeros
D19_LEBENSMITTEL has 837914 (94.0%) zeros Zeros
D19_LOTTO has 490843 (55.1%) zeros Zeros
D19_NAHRUNGSERGAENZUNG has 852176 (95.6%) zeros Zeros
D19_RATGEBER has 805071 (90.3%) zeros Zeros
D19_REISEN has 736924 (82.7%) zeros Zeros
D19_SAMMELARTIKEL has 802085 (90.0%) zeros Zeros
D19_SCHUHE has 773024 (86.7%) zeros Zeros
D19_SONSTIGE has 505953 (56.8%) zeros Zeros
D19_SOZIALES has 505828 (56.8%) zeros Zeros
D19_TECHNIK has 630101 (70.7%) zeros Zeros
D19_TELKO_ANZ_12 has 857990 (96.3%) zeros Zeros
D19_TELKO_ANZ_24 has 826208 (92.7%) zeros Zeros
D19_TELKO_MOBILE has 726804 (81.6%) zeros Zeros
D19_TELKO_REST has 765973 (85.9%) zeros Zeros
D19_TIERARTIKEL has 852220 (95.6%) zeros Zeros
D19_VERSAND_ANZ_12 has 637972 (71.6%) zeros Zeros
D19_VERSAND_ANZ_24 has 563818 (63.3%) zeros Zeros
D19_VERSAND_ONLINE_QUOTE_12 has 417367 (46.8%) zeros Zeros
D19_VERSAND_REST has 734442 (82.4%) zeros Zeros
D19_VERSI_ANZ_12 has 821289 (92.2%) zeros Zeros
D19_VERSI_ANZ_24 has 777037 (87.2%) zeros Zeros
D19_VERSI_ONLINE_QUOTE_12 has 632462 (71.0%) zeros Zeros
D19_VERSICHERUNGEN has 654664 (73.5%) zeros Zeros
D19_VOLLSORTIMENT has 600002 (67.3%) zeros Zeros
D19_WEIN_FEINKOST has 836142 (93.8%) zeros Zeros
GEBURTSJAHR has 392318 (44.0%) zeros Zeros
KBA05_ALTER1 has 102789 (11.5%) zeros Zeros
KBA05_ALTER4 has 50127 (5.6%) zeros Zeros
KBA05_BAUMAX has 343200 (38.5%) zeros Zeros
KBA05_CCM4 has 274064 (30.8%) zeros Zeros
KBA05_DIESEL has 64600 (7.2%) zeros Zeros
KBA05_HERST1 has 75567 (8.5%) zeros Zeros
KBA05_HERST3 has 16789 (1.9%) zeros Zeros
KBA05_HERST4 has 30739 (3.4%) zeros Zeros
KBA05_HERST5 has 47295 (5.3%) zeros Zeros
KBA05_KW3 has 206843 (23.2%) zeros Zeros
KBA05_MOD1 has 286087 (32.1%) zeros Zeros
KBA05_MOD4 has 50590 (5.7%) zeros Zeros
KBA05_SEG10 has 111769 (12.5%) zeros Zeros
KBA05_SEG5 has 182816 (20.5%) zeros Zeros
KBA05_VORB2 has 54357 (6.1%) zeros Zeros
KBA05_ZUL3 has 71287 (8.0%) zeros Zeros
KBA05_ZUL4 has 105584 (11.8%) zeros Zeros
KBA13_BJ_2008 has 134372 (15.1%) zeros Zeros
KBA13_BJ_2009 has 101115 (11.3%) zeros Zeros
KBA13_CCM_0_1400 has 138711 (15.6%) zeros Zeros
KBA13_CCM_1000 has 103227 (11.6%) zeros Zeros
KBA13_CCM_1200 has 145802 (16.4%) zeros Zeros
KBA13_CCM_1800 has 137534 (15.4%) zeros Zeros
KBA13_CCM_2500 has 95350 (10.7%) zeros Zeros
KBA13_CCM_2501 has 93403 (10.5%) zeros Zeros
KBA13_CCM_3000 has 56562 (6.3%) zeros Zeros
KBA13_KMH_0_140 has 96010 (10.8%) zeros Zeros
KBA13_KMH_211 has 139463 (15.6%) zeros Zeros
KBA13_KMH_250 has 139756 (15.7%) zeros Zeros
KBA13_KW_110 has 124216 (13.9%) zeros Zeros
KBA13_KW_120 has 85028 (9.5%) zeros Zeros
KBA13_KW_121 has 95195 (10.7%) zeros Zeros
KBA13_KW_40 has 99260 (11.1%) zeros Zeros
KBA13_KW_50 has 143215 (16.1%) zeros Zeros
KBA13_KW_60 has 132144 (14.8%) zeros Zeros
KBA13_KW_70 has 141548 (15.9%) zeros Zeros
KBA13_KW_80 has 129268 (14.5%) zeros Zeros
KBA13_KW_90 has 133326 (15.0%) zeros Zeros
KBA13_SEG_OBERKLASSE has 86270 (9.7%) zeros Zeros
KBA13_SEG_SPORTWAGEN has 83421 (9.4%) zeros Zeros
KBA13_SEG_WOHNMOBILE has 85796 (9.6%) zeros Zeros
KBA13_VORB_3 has 143284 (16.1%) zeros Zeros
LP_FAMILIE_FEIN has 72938 (8.2%) zeros Zeros
LP_FAMILIE_GROB has 72938 (8.2%) zeros Zeros
LP_LEBENSPHASE_FEIN has 92778 (10.4%) zeros Zeros
LP_LEBENSPHASE_GROB has 89718 (10.1%) zeros Zeros
ONLINE_AFFINITAET has 65716 (7.4%) zeros Zeros
PRAEGENDE_JUGENDJAHRE has 108164 (12.1%) zeros Zeros
REGIOTYP has 36868 (4.1%) zeros Zeros
RT_UEBERGROESSE has 24758 (2.8%) zeros Zeros
TITEL_KZ has 815562 (91.5%) zeros Zeros
VERDICHTUNGSRAUM has 368782 (41.4%) zeros Zeros
VHA has 665547 (74.7%) zeros Zeros
W_KEIT_KIND_HH has 40386 (4.5%) zeros Zeros

Reproduction

Analysis started2021-12-24 10:53:26.143697
Analysis finished2021-12-24 11:04:22.094300
Duration10 minutes and 55.95 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

LNR
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct891221
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean637263
Minimum191653
Maximum1082873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:22.355743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum191653
5-th percentile236214
Q1414458
median637263
Q3860068
95-th percentile1038312
Maximum1082873
Range891220
Interquartile range (IQR)445610

Descriptive statistics

Standard deviation257273.4865
Coefficient of variation (CV)0.4037163408
Kurtosis-1.2
Mean637263
Median Absolute Deviation (MAD)222805
Skewness-1.551882184 × 10-18
Sum5.679421681 × 1011
Variance6.618964684 × 1010
MonotonicityNot monotonic
2021-12-24T14:04:22.566635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1924181
 
< 0.1%
6114551
 
< 0.1%
9821541
 
< 0.1%
9760091
 
< 0.1%
9780561
 
< 0.1%
9555271
 
< 0.1%
9575741
 
< 0.1%
9514291
 
< 0.1%
9534761
 
< 0.1%
9637151
 
< 0.1%
Other values (891211)891211
> 99.9%
ValueCountFrequency (%)
1916531
< 0.1%
1916541
< 0.1%
1916551
< 0.1%
1916561
< 0.1%
1916571
< 0.1%
1916581
< 0.1%
1916591
< 0.1%
1916601
< 0.1%
1916611
< 0.1%
1916621
< 0.1%
ValueCountFrequency (%)
10828731
< 0.1%
10828721
< 0.1%
10828711
< 0.1%
10828701
< 0.1%
10828691
< 0.1%
10828681
< 0.1%
10828671
< 0.1%
10828661
< 0.1%
10828651
< 0.1%
10828641
< 0.1%

AGER_TYP
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
-1
677503 
2
98472 
1
79802 
3
 
27104
0
 
8340

Length

Max length2
Median length2
Mean length1.760196405
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row2
5th row-1

Common Values

ValueCountFrequency (%)
-1677503
76.0%
298472
 
11.0%
179802
 
9.0%
327104
 
3.0%
08340
 
0.9%

Length

2021-12-24T14:04:22.757674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:22.868289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1757305
85.0%
298472
 
11.0%
327104
 
3.0%
08340
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AKT_DAT_KL
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing73499
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean4.421928235
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:22.999073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.638805141
Coefficient of variation (CV)0.8229000896
Kurtosis-1.765419122
Mean4.421928235
Median Absolute Deviation (MAD)2
Skewness0.2873452378
Sum3615908
Variance13.24090285
MonotonicityNot monotonic
2021-12-24T14:04:23.129873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1390258
43.8%
9270663
30.4%
529203
 
3.3%
627655
 
3.1%
324880
 
2.8%
421466
 
2.4%
721026
 
2.4%
817485
 
2.0%
215086
 
1.7%
(Missing)73499
 
8.2%
ValueCountFrequency (%)
1390258
43.8%
215086
 
1.7%
324880
 
2.8%
421466
 
2.4%
529203
 
3.3%
627655
 
3.1%
721026
 
2.4%
817485
 
2.0%
9270663
30.4%
ValueCountFrequency (%)
9270663
30.4%
817485
 
2.0%
721026
 
2.4%
627655
 
3.1%
529203
 
3.3%
421466
 
2.4%
324880
 
2.8%
215086
 
1.7%
1390258
43.8%

ALTER_HH
Real number (ℝ≥0)

MISSING
ZEROS

Distinct22
Distinct (%)< 0.1%
Missing73499
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean10.86412619
Minimum0
Maximum21
Zeros236768
Zeros (%)26.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:23.308940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q317
95-th percentile21
Maximum21
Range21
Interquartile range (IQR)17

Descriptive statistics

Standard deviation7.639683284
Coefficient of variation (CV)0.7032027378
Kurtosis-1.364928569
Mean10.86412619
Median Absolute Deviation (MAD)5
Skewness-0.4258583512
Sum8883835
Variance58.36476068
MonotonicityNot monotonic
2021-12-24T14:04:23.461893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0236768
26.6%
1860852
 
6.8%
1755665
 
6.2%
1952890
 
5.9%
1551867
 
5.8%
1651857
 
5.8%
1444275
 
5.0%
2141610
 
4.7%
2040671
 
4.6%
1337612
 
4.2%
Other values (12)143655
16.1%
(Missing)73499
 
8.2%
ValueCountFrequency (%)
0236768
26.6%
11
 
< 0.1%
247
 
< 0.1%
3200
 
< 0.1%
4603
 
0.1%
51030
 
0.1%
63809
 
0.4%
78419
 
0.9%
813463
 
1.5%
922817
 
2.6%
ValueCountFrequency (%)
2141610
4.7%
2040671
4.6%
1952890
5.9%
1860852
6.8%
1755665
6.2%
1651857
5.8%
1551867
5.8%
1444275
5.0%
1337612
4.2%
1234923
3.9%

ALTER_KIND1
Real number (ℝ≥0)

MISSING

Distinct17
Distinct (%)< 0.1%
Missing810163
Missing (%)90.9%
Infinite0
Infinite (%)0.0%
Mean11.74539219
Minimum2
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:23.602824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median12
Q315
95-th percentile18
Maximum18
Range16
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.097660168
Coefficient of variation (CV)0.3488738479
Kurtosis-1.03258355
Mean11.74539219
Median Absolute Deviation (MAD)4
Skewness-0.1435969542
Sum952058
Variance16.79081886
MonotonicityNot monotonic
2021-12-24T14:04:24.075672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
186703
 
0.8%
176394
 
0.7%
86343
 
0.7%
76249
 
0.7%
166124
 
0.7%
156008
 
0.7%
145992
 
0.7%
95846
 
0.7%
135713
 
0.6%
105678
 
0.6%
Other values (7)20008
 
2.2%
(Missing)810163
90.9%
ValueCountFrequency (%)
2403
 
< 0.1%
31063
 
0.1%
41084
 
0.1%
51501
 
0.2%
64875
0.5%
76249
0.7%
86343
0.7%
95846
0.7%
105678
0.6%
115506
0.6%
ValueCountFrequency (%)
186703
0.8%
176394
0.7%
166124
0.7%
156008
0.7%
145992
0.7%
135713
0.6%
125576
0.6%
115506
0.6%
105678
0.6%
95846
0.7%

ALTER_KIND2
Real number (ℝ≥0)

MISSING

Distinct17
Distinct (%)0.1%
Missing861722
Missing (%)96.7%
Infinite0
Infinite (%)0.0%
Mean13.40265772
Minimum2
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:24.254687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q111
median14
Q316
95-th percentile18
Maximum18
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.243299584
Coefficient of variation (CV)0.2419892869
Kurtosis-0.6028118253
Mean13.40265772
Median Absolute Deviation (MAD)2
Skewness-0.4232316077
Sum395365
Variance10.51899219
MonotonicityNot monotonic
2021-12-24T14:04:24.397445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
183128
 
0.4%
143111
 
0.3%
173085
 
0.3%
153083
 
0.3%
163010
 
0.3%
132968
 
0.3%
122628
 
0.3%
112450
 
0.3%
101953
 
0.2%
91641
 
0.2%
Other values (7)2442
 
0.3%
(Missing)861722
96.7%
ValueCountFrequency (%)
24
 
< 0.1%
315
 
< 0.1%
467
 
< 0.1%
5154
 
< 0.1%
6396
 
< 0.1%
7627
 
0.1%
81179
0.1%
91641
0.2%
101953
0.2%
112450
0.3%
ValueCountFrequency (%)
183128
0.4%
173085
0.3%
163010
0.3%
153083
0.3%
143111
0.3%
132968
0.3%
122628
0.3%
112450
0.3%
101953
0.2%
91641
0.2%

ALTER_KIND3
Real number (ℝ≥0)

MISSING

Distinct15
Distinct (%)0.2%
Missing885051
Missing (%)99.3%
Infinite0
Infinite (%)0.0%
Mean14.47601297
Minimum4
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:24.588218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9
Q113
median15
Q317
95-th percentile18
Maximum18
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.712427345
Coefficient of variation (CV)0.1873739235
Kurtosis-0.05238128374
Mean14.47601297
Median Absolute Deviation (MAD)2
Skewness-0.6787809182
Sum89317
Variance7.357262104
MonotonicityNot monotonic
2021-12-24T14:04:24.739064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
18866
 
0.1%
15847
 
0.1%
16841
 
0.1%
17826
 
0.1%
14746
 
0.1%
13674
 
0.1%
12438
 
< 0.1%
11363
 
< 0.1%
10237
 
< 0.1%
9159
 
< 0.1%
Other values (5)173
 
< 0.1%
(Missing)885051
99.3%
ValueCountFrequency (%)
42
 
< 0.1%
58
 
< 0.1%
621
 
< 0.1%
740
 
< 0.1%
8102
 
< 0.1%
9159
 
< 0.1%
10237
 
< 0.1%
11363
< 0.1%
12438
< 0.1%
13674
0.1%
ValueCountFrequency (%)
18866
0.1%
17826
0.1%
16841
0.1%
15847
0.1%
14746
0.1%
13674
0.1%
12438
< 0.1%
11363
< 0.1%
10237
 
< 0.1%
9159
 
< 0.1%

ALTER_KIND4
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)1.0%
Missing890016
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean15.08962656
Minimum7
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:24.920108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile10
Q114
median15
Q317
95-th percentile18
Maximum18
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.452932399
Coefficient of variation (CV)0.1625575285
Kurtosis-0.04864460676
Mean15.08962656
Median Absolute Deviation (MAD)2
Skewness-0.7730811365
Sum18183
Variance6.016877352
MonotonicityNot monotonic
2021-12-24T14:04:25.040840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
17225
 
< 0.1%
18216
 
< 0.1%
15171
 
< 0.1%
16159
 
< 0.1%
14136
 
< 0.1%
13119
 
< 0.1%
1259
 
< 0.1%
1148
 
< 0.1%
1042
 
< 0.1%
915
 
< 0.1%
Other values (2)15
 
< 0.1%
(Missing)890016
99.9%
ValueCountFrequency (%)
71
 
< 0.1%
814
 
< 0.1%
915
 
< 0.1%
1042
 
< 0.1%
1148
 
< 0.1%
1259
 
< 0.1%
13119
< 0.1%
14136
< 0.1%
15171
< 0.1%
16159
< 0.1%
ValueCountFrequency (%)
18216
< 0.1%
17225
< 0.1%
16159
< 0.1%
15171
< 0.1%
14136
< 0.1%
13119
< 0.1%
1259
 
< 0.1%
1148
 
< 0.1%
1042
 
< 0.1%
915
 
< 0.1%

ALTERSKATEGORIE_FEIN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct26
Distinct (%)< 0.1%
Missing262947
Missing (%)29.5%
Infinite0
Infinite (%)0.0%
Mean13.70071657
Minimum0
Maximum25
Zeros41188
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:25.191668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median14
Q317
95-th percentile20
Maximum25
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.079849415
Coefficient of variation (CV)0.3707725352
Kurtosis1.198872173
Mean13.70071657
Median Absolute Deviation (MAD)3
Skewness-1.047231753
Sum8607804
Variance25.80487008
MonotonicityNot monotonic
2021-12-24T14:04:25.392814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1563486
 
7.1%
1459709
 
6.7%
1653384
 
6.0%
1851365
 
5.8%
1750011
 
5.6%
1349556
 
5.6%
1242951
 
4.8%
1942340
 
4.8%
041188
 
4.6%
1034903
 
3.9%
Other values (16)139381
15.6%
(Missing)262947
29.5%
ValueCountFrequency (%)
041188
4.6%
11
 
< 0.1%
264
 
< 0.1%
3218
 
< 0.1%
4636
 
0.1%
5994
 
0.1%
63754
 
0.4%
78578
 
1.0%
814516
 
1.6%
926204
2.9%
ValueCountFrequency (%)
251017
 
0.1%
242340
 
0.3%
232838
 
0.3%
223669
 
0.4%
2113658
 
1.5%
2027833
3.1%
1942340
4.8%
1851365
5.8%
1750011
5.6%
1653384
6.0%

ANZ_HAUSHALTE_AKTIV
Real number (ℝ≥0)

MISSING

Distinct292
Distinct (%)< 0.1%
Missing93148
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean8.287263195
Minimum0
Maximum595
Zeros6463
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:25.624212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median4
Q39
95-th percentile28
Maximum595
Range595
Interquartile range (IQR)8

Descriptive statistics

Standard deviation15.62808702
Coefficient of variation (CV)1.885795907
Kurtosis142.6179559
Mean8.287263195
Median Absolute Deviation (MAD)3
Skewness8.779951735
Sum6613841
Variance244.2371038
MonotonicityNot monotonic
2021-12-24T14:04:25.855503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1195957
22.0%
2120982
13.6%
362575
 
7.0%
443213
 
4.8%
537815
 
4.2%
636020
 
4.0%
734526
 
3.9%
832293
 
3.6%
929002
 
3.3%
1025428
 
2.9%
Other values (282)180262
20.2%
(Missing)93148
10.5%
ValueCountFrequency (%)
06463
 
0.7%
1195957
22.0%
2120982
13.6%
362575
 
7.0%
443213
 
4.8%
537815
 
4.2%
636020
 
4.0%
734526
 
3.9%
832293
 
3.6%
929002
 
3.3%
ValueCountFrequency (%)
5958
< 0.1%
5361
 
< 0.1%
5234
< 0.1%
5154
< 0.1%
4457
< 0.1%
4389
< 0.1%
4306
< 0.1%
4143
 
< 0.1%
4042
 
< 0.1%
3953
 
< 0.1%

ANZ_HH_TITEL
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct21
Distinct (%)< 0.1%
Missing97008
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean0.04064652681
Minimum0
Maximum23
Zeros770244
Zeros (%)86.4%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:26.076978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum23
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3240284646
Coefficient of variation (CV)7.971861066
Kurtosis894.8859324
Mean0.04064652681
Median Absolute Deviation (MAD)0
Skewness22.71869357
Sum32282
Variance0.1049944458
MonotonicityNot monotonic
2021-12-24T14:04:26.247871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0770244
86.4%
120157
 
2.3%
22459
 
0.3%
3585
 
0.1%
4232
 
< 0.1%
5117
 
< 0.1%
6106
 
< 0.1%
868
 
< 0.1%
765
 
< 0.1%
934
 
< 0.1%
Other values (11)146
 
< 0.1%
(Missing)97008
 
10.9%
ValueCountFrequency (%)
0770244
86.4%
120157
 
2.3%
22459
 
0.3%
3585
 
0.1%
4232
 
< 0.1%
5117
 
< 0.1%
6106
 
< 0.1%
765
 
< 0.1%
868
 
< 0.1%
934
 
< 0.1%
ValueCountFrequency (%)
233
 
< 0.1%
209
 
< 0.1%
186
 
< 0.1%
1713
< 0.1%
163
 
< 0.1%
157
 
< 0.1%
1416
< 0.1%
1329
< 0.1%
1222
< 0.1%
1122
< 0.1%

ANZ_KINDER
Real number (ℝ≥0)

MISSING
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing73499
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean0.1540181137
Minimum0
Maximum11
Zeros731242
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:26.388630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5023888664
Coefficient of variation (CV)3.261881698
Kurtosis18.38960401
Mean0.1540181137
Median Absolute Deviation (MAD)0
Skewness3.922593655
Sum125944
Variance0.2523945731
MonotonicityNot monotonic
2021-12-24T14:04:26.529491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0731242
82.0%
155350
 
6.2%
224445
 
2.7%
35376
 
0.6%
41057
 
0.1%
5190
 
< 0.1%
647
 
< 0.1%
710
 
< 0.1%
93
 
< 0.1%
111
 
< 0.1%
(Missing)73499
 
8.2%
ValueCountFrequency (%)
0731242
82.0%
155350
 
6.2%
224445
 
2.7%
35376
 
0.6%
41057
 
0.1%
5190
 
< 0.1%
647
 
< 0.1%
710
 
< 0.1%
81
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
93
 
< 0.1%
81
 
< 0.1%
710
 
< 0.1%
647
 
< 0.1%
5190
 
< 0.1%
41057
 
0.1%
35376
 
0.6%
224445
2.7%
155350
6.2%

ANZ_PERSONEN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct30
Distinct (%)< 0.1%
Missing73499
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean1.727637265
Minimum0
Maximum45
Zeros34103
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:26.700365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum45
Range45
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.155849337
Coefficient of variation (CV)0.6690347329
Kurtosis14.44942207
Mean1.727637265
Median Absolute Deviation (MAD)0
Skewness1.875265299
Sum1412727
Variance1.335987689
MonotonicityNot monotonic
2021-12-24T14:04:26.851196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1423383
47.5%
2195470
21.9%
394905
 
10.6%
447126
 
5.3%
034103
 
3.8%
515503
 
1.7%
64842
 
0.5%
71525
 
0.2%
8523
 
0.1%
9180
 
< 0.1%
Other values (20)162
 
< 0.1%
(Missing)73499
 
8.2%
ValueCountFrequency (%)
034103
 
3.8%
1423383
47.5%
2195470
21.9%
394905
 
10.6%
447126
 
5.3%
515503
 
1.7%
64842
 
0.5%
71525
 
0.2%
8523
 
0.1%
9180
 
< 0.1%
ValueCountFrequency (%)
451
 
< 0.1%
401
 
< 0.1%
382
< 0.1%
372
< 0.1%
351
 
< 0.1%
311
 
< 0.1%
291
 
< 0.1%
232
< 0.1%
222
< 0.1%
214
< 0.1%

ANZ_STATISTISCHE_HAUSHALTE
Real number (ℝ≥0)

MISSING

Distinct268
Distinct (%)< 0.1%
Missing93148
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean7.599356199
Minimum0
Maximum449
Zeros43
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:27.062474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q39
95-th percentile26
Maximum449
Range449
Interquartile range (IQR)8

Descriptive statistics

Standard deviation14.33220143
Coefficient of variation (CV)1.88597574
Kurtosis126.886353
Mean7.599356199
Median Absolute Deviation (MAD)2
Skewness8.514017811
Sum6064841
Variance205.4119979
MonotonicityNot monotonic
2021-12-24T14:04:27.263630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1219119
24.6%
2121485
13.6%
361478
 
6.9%
444864
 
5.0%
540133
 
4.5%
638466
 
4.3%
736589
 
4.1%
832816
 
3.7%
928500
 
3.2%
1024100
 
2.7%
Other values (258)150523
16.9%
(Missing)93148
10.5%
ValueCountFrequency (%)
043
 
< 0.1%
1219119
24.6%
2121485
13.6%
361478
 
6.9%
444864
 
5.0%
540133
 
4.5%
638466
 
4.3%
736589
 
4.1%
832816
 
3.7%
928500
 
3.2%
ValueCountFrequency (%)
4493
 
< 0.1%
4457
< 0.1%
3756
< 0.1%
3713
 
< 0.1%
3699
< 0.1%
3679
< 0.1%
3667
< 0.1%
3659
< 0.1%
3549
< 0.1%
3538
< 0.1%

ANZ_TITEL
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing73499
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean0.00416156102
Minimum0
Maximum6
Zeros814542
Zeros (%)91.4%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:27.455750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06885512372
Coefficient of variation (CV)16.54550381
Kurtosis450.2677478
Mean0.00416156102
Median Absolute Deviation (MAD)0
Skewness18.79279598
Sum3403
Variance0.004741028062
MonotonicityNot monotonic
2021-12-24T14:04:27.615664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0814542
91.4%
12970
 
0.3%
2202
 
< 0.1%
35
 
< 0.1%
42
 
< 0.1%
61
 
< 0.1%
(Missing)73499
 
8.2%
ValueCountFrequency (%)
0814542
91.4%
12970
 
0.3%
2202
 
< 0.1%
35
 
< 0.1%
42
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
42
 
< 0.1%
35
 
< 0.1%
2202
 
< 0.1%
12970
 
0.3%
0814542
91.4%

ARBEIT
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing97216
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean3.167854107
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:27.774496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q34
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0023762
Coefficient of variation (CV)0.3164212008
Kurtosis-0.2117945621
Mean3.167854107
Median Absolute Deviation (MAD)1
Skewness-0.4612907983
Sum2515292
Variance1.004758047
MonotonicityNot monotonic
2021-12-24T14:04:27.927323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4311339
34.9%
3254988
28.6%
2135662
15.2%
156767
 
6.4%
535090
 
3.9%
9159
 
< 0.1%
(Missing)97216
 
10.9%
ValueCountFrequency (%)
156767
 
6.4%
2135662
15.2%
3254988
28.6%
4311339
34.9%
535090
 
3.9%
9159
 
< 0.1%
ValueCountFrequency (%)
9159
 
< 0.1%
535090
 
3.9%
4311339
34.9%
3254988
28.6%
2135662
15.2%
156767
 
6.4%

BALLRAUM
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing93740
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean4.153043145
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:28.198990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.183709883
Coefficient of variation (CV)0.5258095827
Kurtosis-1.517783243
Mean4.153043145
Median Absolute Deviation (MAD)2
Skewness-0.2395397181
Sum3311973
Variance4.768588851
MonotonicityNot monotonic
2021-12-24T14:04:28.329722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6255093
28.6%
1151782
17.0%
2104521
11.7%
799039
 
11.1%
373277
 
8.2%
461358
 
6.9%
552411
 
5.9%
(Missing)93740
 
10.5%
ValueCountFrequency (%)
1151782
17.0%
2104521
11.7%
373277
 
8.2%
461358
 
6.9%
552411
 
5.9%
6255093
28.6%
799039
 
11.1%
ValueCountFrequency (%)
799039
 
11.1%
6255093
28.6%
552411
 
5.9%
461358
 
6.9%
373277
 
8.2%
2104521
11.7%
1151782
17.0%

CAMEO_DEU_2015
Categorical

MISSING

Distinct45
Distinct (%)< 0.1%
Missing98979
Missing (%)11.1%
Memory size6.8 MiB
6B
56672 
8A
 
52438
4C
 
47819
2D
 
35074
3C
 
34769
Other values (40)
565470 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8A
2nd row4C
3rd row2A
4th row6B
5th row8C

Common Values

ValueCountFrequency (%)
6B56672
 
6.4%
8A52438
 
5.9%
4C47819
 
5.4%
2D35074
 
3.9%
3C34769
 
3.9%
7A34399
 
3.9%
3D34307
 
3.8%
8B33434
 
3.8%
4A33155
 
3.7%
8C30993
 
3.5%
Other values (35)399182
44.8%
(Missing)98979
 
11.1%

Length

2021-12-24T14:04:28.490482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6b56672
 
7.2%
8a52438
 
6.6%
4c47819
 
6.0%
2d35074
 
4.4%
3c34769
 
4.4%
7a34399
 
4.3%
3d34307
 
4.3%
8b33434
 
4.2%
4a33155
 
4.2%
8c30993
 
3.9%
Other values (35)399182
50.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CAMEO_DEUG_2015
Categorical

MISSING

Distinct10
Distinct (%)< 0.1%
Missing98979
Missing (%)11.1%
Memory size6.8 MiB
8
134441 
9
108177 
6
105874 
4
103912 
3
86779 
Other values (5)
253059 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row4
3rd row2
4th row6
5th row8

Common Values

ValueCountFrequency (%)
8134441
15.1%
9108177
12.1%
6105874
11.9%
4103912
11.7%
386779
9.7%
283231
9.3%
777933
8.7%
555310
6.2%
136212
 
4.1%
X373
 
< 0.1%
(Missing)98979
11.1%

Length

2021-12-24T14:04:28.661436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:28.802231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
8134441
17.0%
9108177
13.7%
6105874
13.4%
4103912
13.1%
386779
11.0%
283231
10.5%
777933
9.8%
555310
7.0%
136212
 
4.6%
x373
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CAMEO_INTL_2015
Categorical

MISSING

Distinct22
Distinct (%)< 0.1%
Missing98979
Missing (%)11.1%
Memory size6.8 MiB
51
133694 
41
92336 
24
91158 
14
62884 
43
56672 
Other values (17)
355498 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row51
2nd row24
3rd row12
4th row43
5th row54

Common Values

ValueCountFrequency (%)
51133694
15.0%
4192336
10.4%
2491158
10.2%
1462884
 
7.1%
4356672
 
6.4%
5445391
 
5.1%
2539628
 
4.4%
2233155
 
3.7%
2326750
 
3.0%
1326336
 
3.0%
Other values (12)184238
20.7%
(Missing)98979
11.1%

Length

2021-12-24T14:04:29.043572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
51133694
16.9%
4192336
11.7%
2491158
11.5%
1462884
 
7.9%
4356672
 
7.2%
5445391
 
5.7%
2539628
 
5.0%
2233155
 
4.2%
2326750
 
3.4%
1326336
 
3.3%
Other values (12)184238
23.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_GESAMTTYP
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean3.632838316
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:29.204417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.595021092
Coefficient of variation (CV)0.4390564493
Kurtosis-1.068626824
Mean3.632838316
Median Absolute Deviation (MAD)1
Skewness-0.03888466953
Sum3220028
Variance2.544092284
MonotonicityNot monotonic
2021-12-24T14:04:29.353229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4210963
23.7%
3156449
17.6%
6153915
17.3%
2148795
16.7%
5117376
13.2%
198869
11.1%
(Missing)4854
 
0.5%
ValueCountFrequency (%)
198869
11.1%
2148795
16.7%
3156449
17.6%
4210963
23.7%
5117376
13.2%
6153915
17.3%
ValueCountFrequency (%)
6153915
17.3%
5117376
13.2%
4210963
23.7%
3156449
17.6%
2148795
16.7%
198869
11.1%
Distinct5
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Memory size6.8 MiB
5.0
281804 
4.0
174275 
1.0
167426 
3.0
156998 
2.0
105864 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0281804
31.6%
4.0174275
19.6%
1.0167426
18.8%
3.0156998
17.6%
2.0105864
 
11.9%
(Missing)4854
 
0.5%

Length

2021-12-24T14:04:29.534248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:29.636842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0281804
31.8%
4.0174275
19.7%
1.0167426
18.9%
3.0156998
17.7%
2.0105864
 
11.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_1
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Memory size6.8 MiB
5.0
267488 
2.0
196545 
3.0
171838 
4.0
162940 
1.0
87556 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row5.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0267488
30.0%
2.0196545
22.1%
3.0171838
19.3%
4.0162940
18.3%
1.087556
 
9.8%
(Missing)4854
 
0.5%

Length

2021-12-24T14:04:29.777650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:29.888246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0267488
30.2%
2.0196545
22.2%
3.0171838
19.4%
4.0162940
18.4%
1.087556
 
9.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_2
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Memory size6.8 MiB
5.0
233302 
2.0
205425 
3.0
173910 
4.0
153045 
1.0
120685 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row5.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0233302
26.2%
2.0205425
23.0%
3.0173910
19.5%
4.0153045
17.2%
1.0120685
13.5%
(Missing)4854
 
0.5%

Length

2021-12-24T14:04:30.059225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:30.177885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0233302
26.3%
2.0205425
23.2%
3.0173910
19.6%
4.0153045
17.3%
1.0120685
13.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_3
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Memory size6.8 MiB
5.0
270143 
2.0
181803 
3.0
170162 
4.0
160469 
1.0
103790 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row2.0
3rd row1.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0270143
30.3%
2.0181803
20.4%
3.0170162
19.1%
4.0160469
18.0%
1.0103790
 
11.6%
(Missing)4854
 
0.5%

Length

2021-12-24T14:04:30.318685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:30.411212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0270143
30.5%
2.0181803
20.5%
3.0170162
19.2%
4.0160469
18.1%
1.0103790
 
11.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_4
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Memory size6.8 MiB
5.0
254763 
3.0
180975 
2.0
180312 
4.0
169791 
1.0
100526 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.0
3rd row3.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
5.0254763
28.6%
3.0180975
20.3%
2.0180312
20.2%
4.0169791
19.1%
1.0100526
 
11.3%
(Missing)4854
 
0.5%

Length

2021-12-24T14:04:30.541929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:30.640508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0254763
28.7%
3.0180975
20.4%
2.0180312
20.3%
4.0169791
19.2%
1.0100526
 
11.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_5
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Memory size6.8 MiB
5.0
271673 
3.0
194636 
2.0
174808 
4.0
147220 
1.0
98030 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row2.0
4th row5.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0271673
30.5%
3.0194636
21.8%
2.0174808
19.6%
4.0147220
16.5%
1.098030
 
11.0%
(Missing)4854
 
0.5%

Length

2021-12-24T14:04:30.783350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:30.883898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0271673
30.7%
3.0194636
22.0%
2.0174808
19.7%
4.0147220
16.6%
1.098030
 
11.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_6
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Memory size6.8 MiB
5.0
272208 
4.0
193430 
2.0
175179 
3.0
170731 
1.0
74819 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0272208
30.5%
4.0193430
21.7%
2.0175179
19.7%
3.0170731
19.2%
1.074819
 
8.4%
(Missing)4854
 
0.5%

Length

2021-12-24T14:04:31.025039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:31.105541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0272208
30.7%
4.0193430
21.8%
2.0175179
19.8%
3.0170731
19.3%
1.074819
 
8.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

D19_BANKEN_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1223355374
Minimum0
Maximum6
Zeros831734
Zeros (%)93.3%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:31.256446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.535949819
Coefficient of variation (CV)4.380982258
Kurtosis35.68955582
Mean0.1223355374
Median Absolute Deviation (MAD)0
Skewness5.544272367
Sum109028
Variance0.2872422085
MonotonicityNot monotonic
2021-12-24T14:04:31.387165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0831734
93.3%
129771
 
3.3%
218067
 
2.0%
35708
 
0.6%
44082
 
0.5%
51483
 
0.2%
6376
 
< 0.1%
ValueCountFrequency (%)
0831734
93.3%
129771
 
3.3%
218067
 
2.0%
35708
 
0.6%
44082
 
0.5%
51483
 
0.2%
6376
 
< 0.1%
ValueCountFrequency (%)
6376
 
< 0.1%
51483
 
0.2%
44082
 
0.5%
35708
 
0.6%
218067
 
2.0%
129771
 
3.3%
0831734
93.3%

D19_BANKEN_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2199072957
Minimum0
Maximum6
Zeros794100
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:31.517563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7479025591
Coefficient of variation (CV)3.400990206
Kurtosis19.49388354
Mean0.2199072957
Median Absolute Deviation (MAD)0
Skewness4.197355628
Sum195986
Variance0.5593582379
MonotonicityNot monotonic
2021-12-24T14:04:31.648314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0794100
89.1%
143554
 
4.9%
229079
 
3.3%
310214
 
1.1%
49041
 
1.0%
53930
 
0.4%
61303
 
0.1%
ValueCountFrequency (%)
0794100
89.1%
143554
 
4.9%
229079
 
3.3%
310214
 
1.1%
49041
 
1.0%
53930
 
0.4%
61303
 
0.1%
ValueCountFrequency (%)
61303
 
0.1%
53930
 
0.4%
49041
 
1.0%
310214
 
1.1%
229079
 
3.3%
143554
 
4.9%
0794100
89.1%

D19_BANKEN_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.267419641
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:31.779079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.735724547
Coefficient of variation (CV)0.1872931856
Kurtosis7.988738678
Mean9.267419641
Median Absolute Deviation (MAD)0
Skewness-2.850510488
Sum8259319
Variance3.012739702
MonotonicityNot monotonic
2021-12-24T14:04:31.897755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10678331
76.1%
982707
 
9.3%
833062
 
3.7%
529494
 
3.3%
720482
 
2.3%
617152
 
1.9%
18495
 
1.0%
48406
 
0.9%
28001
 
0.9%
35091
 
0.6%
ValueCountFrequency (%)
18495
 
1.0%
28001
 
0.9%
35091
 
0.6%
48406
 
0.9%
529494
 
3.3%
617152
 
1.9%
720482
 
2.3%
833062
 
3.7%
982707
 
9.3%
10678331
76.1%
ValueCountFrequency (%)
10678331
76.1%
982707
 
9.3%
833062
 
3.7%
720482
 
2.3%
617152
 
1.9%
529494
 
3.3%
48406
 
0.9%
35091
 
0.6%
28001
 
0.9%
18495
 
1.0%

D19_BANKEN_DIREKT
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8927347987
Minimum0
Maximum7
Zeros728811
Zeros (%)81.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:32.018547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.011837672
Coefficient of variation (CV)2.253566989
Kurtosis2.168067482
Mean0.8927347987
Median Absolute Deviation (MAD)0
Skewness1.975549613
Sum795624
Variance4.04749082
MonotonicityNot monotonic
2021-12-24T14:04:32.141509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0728811
81.8%
684798
 
9.5%
327350
 
3.1%
518539
 
2.1%
48771
 
1.0%
28119
 
0.9%
77656
 
0.9%
17177
 
0.8%
ValueCountFrequency (%)
0728811
81.8%
17177
 
0.8%
28119
 
0.9%
327350
 
3.1%
48771
 
1.0%
518539
 
2.1%
684798
 
9.5%
77656
 
0.9%
ValueCountFrequency (%)
77656
 
0.9%
684798
 
9.5%
518539
 
2.1%
48771
 
1.0%
327350
 
3.1%
28119
 
0.9%
17177
 
0.8%
0728811
81.8%

D19_BANKEN_GROSS
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5685795106
Minimum0
Maximum6
Zeros785351
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:32.282553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.643763602
Coefficient of variation (CV)2.891000417
Kurtosis5.732380031
Mean0.5685795106
Median Absolute Deviation (MAD)0
Skewness2.72238652
Sum506730
Variance2.701958781
MonotonicityNot monotonic
2021-12-24T14:04:32.413285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0785351
88.1%
657103
 
6.4%
314862
 
1.7%
513911
 
1.6%
48165
 
0.9%
16347
 
0.7%
25482
 
0.6%
ValueCountFrequency (%)
0785351
88.1%
16347
 
0.7%
25482
 
0.6%
314862
 
1.7%
48165
 
0.9%
513911
 
1.6%
657103
 
6.4%
ValueCountFrequency (%)
657103
 
6.4%
513911
 
1.6%
48165
 
0.9%
314862
 
1.7%
25482
 
0.6%
16347
 
0.7%
0785351
88.1%

D19_BANKEN_LOKAL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1067692525
Minimum0
Maximum7
Zeros874745
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:32.544343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8081790634
Coefficient of variation (CV)7.569398907
Kurtosis60.04532759
Mean0.1067692525
Median Absolute Deviation (MAD)0
Skewness7.777010935
Sum95155
Variance0.6531533985
MonotonicityNot monotonic
2021-12-24T14:04:32.655013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0874745
98.2%
78522
 
1.0%
33500
 
0.4%
63202
 
0.4%
51053
 
0.1%
2118
 
< 0.1%
469
 
< 0.1%
112
 
< 0.1%
ValueCountFrequency (%)
0874745
98.2%
112
 
< 0.1%
2118
 
< 0.1%
33500
 
0.4%
469
 
< 0.1%
51053
 
0.1%
63202
 
0.4%
78522
 
1.0%
ValueCountFrequency (%)
78522
 
1.0%
63202
 
0.4%
51053
 
0.1%
469
 
< 0.1%
33500
 
0.4%
2118
 
< 0.1%
112
 
< 0.1%
0874745
98.2%

D19_BANKEN_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.926793691
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:32.785817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6056413659
Coefficient of variation (CV)0.06101077394
Kurtosis117.0672592
Mean9.926793691
Median Absolute Deviation (MAD)0
Skewness-10.30930608
Sum8846967
Variance0.366801464
MonotonicityNot monotonic
2021-12-24T14:04:33.067562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10871535
97.8%
86451
 
0.7%
95297
 
0.6%
54177
 
0.5%
22058
 
0.2%
6509
 
0.1%
1476
 
0.1%
7335
 
< 0.1%
4311
 
< 0.1%
372
 
< 0.1%
ValueCountFrequency (%)
1476
 
0.1%
22058
 
0.2%
372
 
< 0.1%
4311
 
< 0.1%
54177
 
0.5%
6509
 
0.1%
7335
 
< 0.1%
86451
 
0.7%
95297
 
0.6%
10871535
97.8%
ValueCountFrequency (%)
10871535
97.8%
95297
 
0.6%
86451
 
0.7%
7335
 
< 0.1%
6509
 
0.1%
54177
 
0.5%
4311
 
< 0.1%
372
 
< 0.1%
22058
 
0.2%
1476
 
0.1%

D19_BANKEN_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.439072912
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:33.188301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.54777343
Coefficient of variation (CV)0.1639751535
Kurtosis11.67298613
Mean9.439072912
Median Absolute Deviation (MAD)0
Skewness-3.372331244
Sum8412300
Variance2.395602589
MonotonicityNot monotonic
2021-12-24T14:04:33.288877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10726982
81.6%
966077
 
7.4%
822939
 
2.6%
522124
 
2.5%
716321
 
1.8%
613668
 
1.5%
16917
 
0.8%
46869
 
0.8%
24965
 
0.6%
34359
 
0.5%
ValueCountFrequency (%)
16917
 
0.8%
24965
 
0.6%
34359
 
0.5%
46869
 
0.8%
522124
 
2.5%
613668
 
1.5%
716321
 
1.8%
822939
 
2.6%
966077
 
7.4%
10726982
81.6%
ValueCountFrequency (%)
10726982
81.6%
966077
 
7.4%
822939
 
2.6%
716321
 
1.8%
613668
 
1.5%
522124
 
2.5%
46869
 
0.8%
34359
 
0.5%
24965
 
0.6%
16917
 
0.8%

D19_BANKEN_ONLINE_QUOTE_12
Real number (ℝ≥0)

MISSING
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing257113
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean0.7052205618
Minimum0
Maximum10
Zeros588874
Zeros (%)66.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:33.407639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.552707473
Coefficient of variation (CV)3.619729219
Kurtosis9.269283652
Mean0.7052205618
Median Absolute Deviation (MAD)0
Skewness3.354041234
Sum447186
Variance6.516315445
MonotonicityNot monotonic
2021-12-24T14:04:33.550417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0588874
66.1%
1044065
 
4.9%
5391
 
< 0.1%
3220
 
< 0.1%
7214
 
< 0.1%
8172
 
< 0.1%
967
 
< 0.1%
650
 
< 0.1%
235
 
< 0.1%
418
 
< 0.1%
(Missing)257113
28.8%
ValueCountFrequency (%)
0588874
66.1%
12
 
< 0.1%
235
 
< 0.1%
3220
 
< 0.1%
418
 
< 0.1%
5391
 
< 0.1%
650
 
< 0.1%
7214
 
< 0.1%
8172
 
< 0.1%
967
 
< 0.1%
ValueCountFrequency (%)
1044065
4.9%
967
 
< 0.1%
8172
 
< 0.1%
7214
 
< 0.1%
650
 
< 0.1%
5391
 
< 0.1%
418
 
< 0.1%
3220
 
< 0.1%
235
 
< 0.1%
12
 
< 0.1%

D19_BANKEN_REST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4256452664
Minimum0
Maximum7
Zeros821760
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:33.691183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.510782005
Coefficient of variation (CV)3.549392239
Kurtosis9.772301708
Mean0.4256452664
Median Absolute Deviation (MAD)0
Skewness3.389116791
Sum379344
Variance2.282462268
MonotonicityNot monotonic
2021-12-24T14:04:33.832153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0821760
92.2%
643143
 
4.8%
57744
 
0.9%
77339
 
0.8%
35943
 
0.7%
22928
 
0.3%
41448
 
0.2%
1916
 
0.1%
ValueCountFrequency (%)
0821760
92.2%
1916
 
0.1%
22928
 
0.3%
35943
 
0.7%
41448
 
0.2%
57744
 
0.9%
643143
 
4.8%
77339
 
0.8%
ValueCountFrequency (%)
77339
 
0.8%
643143
 
4.8%
57744
 
0.9%
41448
 
0.2%
35943
 
0.7%
22928
 
0.3%
1916
 
0.1%
0821760
92.2%

D19_BEKLEIDUNG_GEH
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4678693612
Minimum0
Maximum7
Zeros809304
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:33.983089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.542150743
Coefficient of variation (CV)3.296113981
Kurtosis8.575243035
Mean0.4678693612
Median Absolute Deviation (MAD)0
Skewness3.183221612
Sum416975
Variance2.378228914
MonotonicityNot monotonic
2021-12-24T14:04:34.124038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0809304
90.8%
639392
 
4.4%
315239
 
1.7%
511899
 
1.3%
78478
 
1.0%
23117
 
0.3%
42013
 
0.2%
11779
 
0.2%
ValueCountFrequency (%)
0809304
90.8%
11779
 
0.2%
23117
 
0.3%
315239
 
1.7%
42013
 
0.2%
511899
 
1.3%
639392
 
4.4%
78478
 
1.0%
ValueCountFrequency (%)
78478
 
1.0%
639392
 
4.4%
511899
 
1.3%
42013
 
0.2%
315239
 
1.7%
23117
 
0.3%
11779
 
0.2%
0809304
90.8%

D19_BEKLEIDUNG_REST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.145516095
Minimum0
Maximum7
Zeros692502
Zeros (%)77.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:34.285077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.266998592
Coefficient of variation (CV)1.979019414
Kurtosis0.8072089987
Mean1.145516095
Median Absolute Deviation (MAD)0
Skewness1.613705085
Sum1020908
Variance5.139282617
MonotonicityNot monotonic
2021-12-24T14:04:34.396021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0692502
77.7%
6109025
 
12.2%
330201
 
3.4%
719637
 
2.2%
518554
 
2.1%
28748
 
1.0%
17262
 
0.8%
45292
 
0.6%
ValueCountFrequency (%)
0692502
77.7%
17262
 
0.8%
28748
 
1.0%
330201
 
3.4%
45292
 
0.6%
518554
 
2.1%
6109025
 
12.2%
719637
 
2.2%
ValueCountFrequency (%)
719637
 
2.2%
6109025
 
12.2%
518554
 
2.1%
45292
 
0.6%
330201
 
3.4%
28748
 
1.0%
17262
 
0.8%
0692502
77.7%

D19_BILDUNG
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4855080839
Minimum0
Maximum7
Zeros813156
Zeros (%)91.2%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:34.506744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.639339895
Coefficient of variation (CV)3.376545004
Kurtosis8.789013383
Mean0.4855080839
Median Absolute Deviation (MAD)0
Skewness3.236373733
Sum432695
Variance2.687435291
MonotonicityNot monotonic
2021-12-24T14:04:34.637671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0813156
91.2%
637502
 
4.2%
721828
 
2.4%
28582
 
1.0%
35127
 
0.6%
53363
 
0.4%
41288
 
0.1%
1375
 
< 0.1%
ValueCountFrequency (%)
0813156
91.2%
1375
 
< 0.1%
28582
 
1.0%
35127
 
0.6%
41288
 
0.1%
53363
 
0.4%
637502
 
4.2%
721828
 
2.4%
ValueCountFrequency (%)
721828
 
2.4%
637502
 
4.2%
53363
 
0.4%
41288
 
0.1%
35127
 
0.6%
28582
 
1.0%
1375
 
< 0.1%
0813156
91.2%

D19_BIO_OEKO
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2579382667
Minimum0
Maximum7
Zeros854074
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:34.786588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.252328077
Coefficient of variation (CV)4.855146514
Kurtosis20.70782897
Mean0.2579382667
Median Absolute Deviation (MAD)0
Skewness4.732438138
Sum229880
Variance1.568325611
MonotonicityNot monotonic
2021-12-24T14:04:34.909397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0854074
95.8%
617732
 
2.0%
715241
 
1.7%
52121
 
0.2%
31926
 
0.2%
483
 
< 0.1%
242
 
< 0.1%
12
 
< 0.1%
ValueCountFrequency (%)
0854074
95.8%
12
 
< 0.1%
242
 
< 0.1%
31926
 
0.2%
483
 
< 0.1%
52121
 
0.2%
617732
 
2.0%
715241
 
1.7%
ValueCountFrequency (%)
715241
 
1.7%
617732
 
2.0%
52121
 
0.2%
483
 
< 0.1%
31926
 
0.2%
242
 
< 0.1%
12
 
< 0.1%
0854074
95.8%

D19_BUCH_CD
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.585404742
Minimum0
Maximum7
Zeros622788
Zeros (%)69.9%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:35.060301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.547925475
Coefficient of variation (CV)1.60711357
Kurtosis-0.721059398
Mean1.585404742
Median Absolute Deviation (MAD)0
Skewness1.081502492
Sum1412946
Variance6.491924228
MonotonicityNot monotonic
2021-12-24T14:04:35.160901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0622788
69.9%
6188263
 
21.1%
326192
 
2.9%
515900
 
1.8%
111735
 
1.3%
29187
 
1.0%
78853
 
1.0%
48303
 
0.9%
ValueCountFrequency (%)
0622788
69.9%
111735
 
1.3%
29187
 
1.0%
326192
 
2.9%
48303
 
0.9%
515900
 
1.8%
6188263
 
21.1%
78853
 
1.0%
ValueCountFrequency (%)
78853
 
1.0%
6188263
 
21.1%
515900
 
1.8%
48303
 
0.9%
326192
 
2.9%
29187
 
1.0%
111735
 
1.3%
0622788
69.9%

D19_DIGIT_SERV
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1967458128
Minimum0
Maximum7
Zeros857661
Zeros (%)96.2%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:35.310253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.036761626
Coefficient of variation (CV)5.269548618
Kurtosis27.29309336
Mean0.1967458128
Median Absolute Deviation (MAD)0
Skewness5.331820494
Sum175344
Variance1.074874669
MonotonicityNot monotonic
2021-12-24T14:04:35.443980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0857661
96.2%
617942
 
2.0%
36153
 
0.7%
74030
 
0.5%
53225
 
0.4%
21293
 
0.1%
4465
 
0.1%
1452
 
0.1%
ValueCountFrequency (%)
0857661
96.2%
1452
 
0.1%
21293
 
0.1%
36153
 
0.7%
4465
 
0.1%
53225
 
0.4%
617942
 
2.0%
74030
 
0.5%
ValueCountFrequency (%)
74030
 
0.5%
617942
 
2.0%
53225
 
0.4%
4465
 
0.1%
36153
 
0.7%
21293
 
0.1%
1452
 
0.1%
0857661
96.2%

D19_DROGERIEARTIKEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6732920342
Minimum0
Maximum7
Zeros761014
Zeros (%)85.4%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:35.544793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.752757614
Coefficient of variation (CV)2.603265039
Kurtosis4.475394192
Mean0.6732920342
Median Absolute Deviation (MAD)0
Skewness2.4550082
Sum600052
Variance3.072159252
MonotonicityNot monotonic
2021-12-24T14:04:35.685824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0761014
85.4%
652060
 
5.8%
324763
 
2.8%
517548
 
2.0%
410951
 
1.2%
29928
 
1.1%
77841
 
0.9%
17116
 
0.8%
ValueCountFrequency (%)
0761014
85.4%
17116
 
0.8%
29928
 
1.1%
324763
 
2.8%
410951
 
1.2%
517548
 
2.0%
652060
 
5.8%
77841
 
0.9%
ValueCountFrequency (%)
77841
 
0.9%
652060
 
5.8%
517548
 
2.0%
410951
 
1.2%
324763
 
2.8%
29928
 
1.1%
17116
 
0.8%
0761014
85.4%

D19_ENERGIE
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3468544839
Minimum0
Maximum7
Zeros829857
Zeros (%)93.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:35.874965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.337269246
Coefficient of variation (CV)3.855418649
Kurtosis13.41220687
Mean0.3468544839
Median Absolute Deviation (MAD)0
Skewness3.838273892
Sum309124
Variance1.788289036
MonotonicityNot monotonic
2021-12-24T14:04:36.058255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0829857
93.1%
625788
 
2.9%
314572
 
1.6%
59556
 
1.1%
77655
 
0.9%
21967
 
0.2%
41185
 
0.1%
1641
 
0.1%
ValueCountFrequency (%)
0829857
93.1%
1641
 
0.1%
21967
 
0.2%
314572
 
1.6%
41185
 
0.1%
59556
 
1.1%
625788
 
2.9%
77655
 
0.9%
ValueCountFrequency (%)
77655
 
0.9%
625788
 
2.9%
59556
 
1.1%
41185
 
0.1%
314572
 
1.6%
21967
 
0.2%
1641
 
0.1%
0829857
93.1%

D19_FREIZEIT
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5906784064
Minimum0
Maximum7
Zeros790748
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:36.259454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.721602058
Coefficient of variation (CV)2.914618242
Kurtosis5.660377566
Mean0.5906784064
Median Absolute Deviation (MAD)0
Skewness2.712950351
Sum526425
Variance2.963913646
MonotonicityNot monotonic
2021-12-24T14:04:36.430424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0790748
88.7%
655056
 
6.2%
315297
 
1.7%
513587
 
1.5%
78514
 
1.0%
43990
 
0.4%
22676
 
0.3%
11353
 
0.2%
ValueCountFrequency (%)
0790748
88.7%
11353
 
0.2%
22676
 
0.3%
315297
 
1.7%
43990
 
0.4%
513587
 
1.5%
655056
 
6.2%
78514
 
1.0%
ValueCountFrequency (%)
78514
 
1.0%
655056
 
6.2%
513587
 
1.5%
43990
 
0.4%
315297
 
1.7%
22676
 
0.3%
11353
 
0.2%
0790748
88.7%

D19_GARTEN
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2559926214
Minimum0
Maximum7
Zeros851626
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:36.631418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.213047558
Coefficient of variation (CV)4.738603603
Kurtosis20.21717778
Mean0.2559926214
Median Absolute Deviation (MAD)0
Skewness4.662702276
Sum228146
Variance1.471484378
MonotonicityNot monotonic
2021-12-24T14:04:36.792896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0851626
95.6%
620410
 
2.3%
79555
 
1.1%
54979
 
0.6%
34003
 
0.4%
4328
 
< 0.1%
2265
 
< 0.1%
155
 
< 0.1%
ValueCountFrequency (%)
0851626
95.6%
155
 
< 0.1%
2265
 
< 0.1%
34003
 
0.4%
4328
 
< 0.1%
54979
 
0.6%
620410
 
2.3%
79555
 
1.1%
ValueCountFrequency (%)
79555
 
1.1%
620410
 
2.3%
54979
 
0.6%
4328
 
< 0.1%
34003
 
0.4%
2265
 
< 0.1%
155
 
< 0.1%
0851626
95.6%

D19_GESAMT_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7976831785
Minimum0
Maximum6
Zeros584797
Zeros (%)65.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:36.972031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.330717922
Coefficient of variation (CV)1.668228638
Kurtosis1.978641833
Mean0.7976831785
Median Absolute Deviation (MAD)0
Skewness1.68398608
Sum710912
Variance1.770810189
MonotonicityNot monotonic
2021-12-24T14:04:37.084734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0584797
65.6%
199465
 
11.2%
297282
 
10.9%
345685
 
5.1%
443579
 
4.9%
516966
 
1.9%
63447
 
0.4%
ValueCountFrequency (%)
0584797
65.6%
199465
 
11.2%
297282
 
10.9%
345685
 
5.1%
443579
 
4.9%
516966
 
1.9%
63447
 
0.4%
ValueCountFrequency (%)
63447
 
0.4%
516966
 
1.9%
443579
 
4.9%
345685
 
5.1%
297282
 
10.9%
199465
 
11.2%
0584797
65.6%

D19_GESAMT_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.240179484
Minimum0
Maximum6
Zeros505303
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:37.225489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.72786727
Coefficient of variation (CV)1.393239682
Kurtosis0.1227129192
Mean1.240179484
Median Absolute Deviation (MAD)0
Skewness1.176568175
Sum1105274
Variance2.985525303
MonotonicityNot monotonic
2021-12-24T14:04:37.356205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0505303
56.7%
2101785
 
11.4%
186493
 
9.7%
474210
 
8.3%
358554
 
6.6%
546547
 
5.2%
618329
 
2.1%
ValueCountFrequency (%)
0505303
56.7%
186493
 
9.7%
2101785
 
11.4%
358554
 
6.6%
474210
 
8.3%
546547
 
5.2%
618329
 
2.1%
ValueCountFrequency (%)
618329
 
2.1%
546547
 
5.2%
474210
 
8.3%
358554
 
6.6%
2101785
 
11.4%
186493
 
9.7%
0505303
56.7%

D19_GESAMT_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.071743148
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:37.506934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.212714827
Coefficient of variation (CV)0.454303099
Kurtosis-1.051195891
Mean7.071743148
Median Absolute Deviation (MAD)1
Skewness-0.6616137514
Sum6302486
Variance10.32153656
MonotonicityNot monotonic
2021-12-24T14:04:37.688042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10354170
39.7%
998281
 
11.0%
593740
 
10.5%
176009
 
8.5%
259774
 
6.7%
852852
 
5.9%
444031
 
4.9%
642008
 
4.7%
737486
 
4.2%
332870
 
3.7%
ValueCountFrequency (%)
176009
 
8.5%
259774
 
6.7%
332870
 
3.7%
444031
 
4.9%
593740
 
10.5%
642008
 
4.7%
737486
 
4.2%
852852
 
5.9%
998281
 
11.0%
10354170
39.7%
ValueCountFrequency (%)
10354170
39.7%
998281
 
11.0%
852852
 
5.9%
737486
 
4.2%
642008
 
4.7%
593740
 
10.5%
444031
 
4.9%
332870
 
3.7%
259774
 
6.7%
176009
 
8.5%

D19_GESAMT_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.034676023
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:37.879220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q19
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.768926187
Coefficient of variation (CV)0.195792985
Kurtosis5.521696248
Mean9.034676023
Median Absolute Deviation (MAD)0
Skewness-2.355357653
Sum8051893
Variance3.129099854
MonotonicityNot monotonic
2021-12-24T14:04:38.030119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10558558
62.7%
9147456
 
16.5%
869382
 
7.8%
534827
 
3.9%
727784
 
3.1%
622717
 
2.5%
49547
 
1.1%
29188
 
1.0%
16311
 
0.7%
35451
 
0.6%
ValueCountFrequency (%)
16311
 
0.7%
29188
 
1.0%
35451
 
0.6%
49547
 
1.1%
534827
 
3.9%
622717
 
2.5%
727784
 
3.1%
869382
 
7.8%
9147456
 
16.5%
10558558
62.7%
ValueCountFrequency (%)
10558558
62.7%
9147456
 
16.5%
869382
 
7.8%
727784
 
3.1%
622717
 
2.5%
534827
 
3.9%
49547
 
1.1%
35451
 
0.6%
29188
 
1.0%
16311
 
0.7%

D19_GESAMT_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.680381185
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:38.170876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.039866847
Coefficient of variation (CV)0.3957963509
Kurtosis-0.4469555651
Mean7.680381185
Median Absolute Deviation (MAD)0
Skewness-1.001717247
Sum6844917
Variance9.240790446
MonotonicityNot monotonic
2021-12-24T14:04:38.291546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10450995
50.6%
986118
 
9.7%
579773
 
9.0%
157331
 
6.4%
842628
 
4.8%
241391
 
4.6%
636995
 
4.2%
436227
 
4.1%
733434
 
3.8%
326329
 
3.0%
ValueCountFrequency (%)
157331
 
6.4%
241391
 
4.6%
326329
 
3.0%
436227
 
4.1%
579773
 
9.0%
636995
 
4.2%
733434
 
3.8%
842628
 
4.8%
986118
 
9.7%
10450995
50.6%
ValueCountFrequency (%)
10450995
50.6%
986118
 
9.7%
842628
 
4.8%
733434
 
3.8%
636995
 
4.2%
579773
 
9.0%
436227
 
4.1%
326329
 
3.0%
241391
 
4.6%
157331
 
6.4%

D19_GESAMT_ONLINE_QUOTE_12
Real number (ℝ≥0)

MISSING
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing257113
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean3.560951762
Minimum0
Maximum10
Zeros393075
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:38.420257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)10

Descriptive statistics

Standard deviation4.658537684
Coefficient of variation (CV)1.308228248
Kurtosis-1.603932033
Mean3.560951762
Median Absolute Deviation (MAD)0
Skewness0.5886854726
Sum2258028
Variance21.70197335
MonotonicityNot monotonic
2021-12-24T14:04:38.593034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0393075
44.1%
10199906
22.4%
510517
 
1.2%
89467
 
1.1%
76923
 
0.8%
96046
 
0.7%
33543
 
0.4%
61679
 
0.2%
21066
 
0.1%
41017
 
0.1%
(Missing)257113
28.8%
ValueCountFrequency (%)
0393075
44.1%
1869
 
0.1%
21066
 
0.1%
33543
 
0.4%
41017
 
0.1%
510517
 
1.2%
61679
 
0.2%
76923
 
0.8%
89467
 
1.1%
96046
 
0.7%
ValueCountFrequency (%)
10199906
22.4%
96046
 
0.7%
89467
 
1.1%
76923
 
0.8%
61679
 
0.2%
510517
 
1.2%
41017
 
0.1%
33543
 
0.4%
21066
 
0.1%
1869
 
0.1%

D19_HANDWERK
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8419942977
Minimum0
Maximum7
Zeros768381
Zeros (%)86.2%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:38.744109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.119703265
Coefficient of variation (CV)2.517479359
Kurtosis2.691117597
Mean0.8419942977
Median Absolute Deviation (MAD)0
Skewness2.149109132
Sum750403
Variance4.493141932
MonotonicityNot monotonic
2021-12-24T14:04:38.903078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0768381
86.2%
690968
 
10.2%
725319
 
2.8%
53810
 
0.4%
32465
 
0.3%
4184
 
< 0.1%
287
 
< 0.1%
17
 
< 0.1%
ValueCountFrequency (%)
0768381
86.2%
17
 
< 0.1%
287
 
< 0.1%
32465
 
0.3%
4184
 
< 0.1%
53810
 
0.4%
690968
 
10.2%
725319
 
2.8%
ValueCountFrequency (%)
725319
 
2.8%
690968
 
10.2%
53810
 
0.4%
4184
 
< 0.1%
32465
 
0.3%
287
 
< 0.1%
17
 
< 0.1%
0768381
86.2%

D19_HAUS_DEKO
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.017851913
Minimum0
Maximum7
Zeros713100
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:39.106327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.144350331
Coefficient of variation (CV)2.106740974
Kurtosis1.350673895
Mean1.017851913
Median Absolute Deviation (MAD)0
Skewness1.770905762
Sum907131
Variance4.598238343
MonotonicityNot monotonic
2021-12-24T14:04:39.237130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0713100
80.0%
6100720
 
11.3%
331024
 
3.5%
520748
 
2.3%
710278
 
1.2%
27476
 
0.8%
14133
 
0.5%
43742
 
0.4%
ValueCountFrequency (%)
0713100
80.0%
14133
 
0.5%
27476
 
0.8%
331024
 
3.5%
43742
 
0.4%
520748
 
2.3%
6100720
 
11.3%
710278
 
1.2%
ValueCountFrequency (%)
710278
 
1.2%
6100720
 
11.3%
520748
 
2.3%
43742
 
0.4%
331024
 
3.5%
27476
 
0.8%
14133
 
0.5%
0713100
80.0%

D19_KINDERARTIKEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8706572219
Minimum0
Maximum7
Zeros749365
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:39.620095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.077302903
Coefficient of variation (CV)2.38590211
Kurtosis2.530702709
Mean0.8706572219
Median Absolute Deviation (MAD)0
Skewness2.080699659
Sum775948
Variance4.31518735
MonotonicityNot monotonic
2021-12-24T14:04:39.768997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0749365
84.1%
679320
 
8.9%
722597
 
2.5%
316402
 
1.8%
513398
 
1.5%
25842
 
0.7%
43224
 
0.4%
11073
 
0.1%
ValueCountFrequency (%)
0749365
84.1%
11073
 
0.1%
25842
 
0.7%
316402
 
1.8%
43224
 
0.4%
513398
 
1.5%
679320
 
8.9%
722597
 
2.5%
ValueCountFrequency (%)
722597
 
2.5%
679320
 
8.9%
513398
 
1.5%
43224
 
0.4%
316402
 
1.8%
25842
 
0.7%
11073
 
0.1%
0749365
84.1%

D19_KONSUMTYP
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing257113
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean5.424539668
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:39.911898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.234275463
Coefficient of variation (CV)0.5962304012
Kurtosis-1.620701327
Mean5.424539668
Median Absolute Deviation (MAD)4
Skewness-0.07216050728
Sum3439744
Variance10.46053777
MonotonicityNot monotonic
2021-12-24T14:04:40.072871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9254296
28.5%
1117912
13.2%
478262
 
8.8%
656562
 
6.3%
353330
 
6.0%
249324
 
5.5%
524422
 
2.7%
(Missing)257113
28.8%
ValueCountFrequency (%)
1117912
13.2%
249324
 
5.5%
353330
 
6.0%
478262
 
8.8%
524422
 
2.7%
656562
 
6.3%
9254296
28.5%
ValueCountFrequency (%)
9254296
28.5%
656562
 
6.3%
524422
 
2.7%
478262
 
8.8%
353330
 
6.0%
249324
 
5.5%
1117912
13.2%

D19_KONSUMTYP_MAX
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.849228194
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:40.262384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.225761975
Coefficient of variation (CV)0.5514850623
Kurtosis-1.591701794
Mean5.849228194
Median Absolute Deviation (MAD)1
Skewness-0.4339861885
Sum5212955
Variance10.40554032
MonotonicityNot monotonic
2021-12-24T14:04:40.385163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8260285
29.2%
9257113
28.8%
1144570
16.2%
291423
 
10.3%
475752
 
8.5%
362078
 
7.0%
ValueCountFrequency (%)
1144570
16.2%
291423
 
10.3%
362078
 
7.0%
475752
 
8.5%
8260285
29.2%
9257113
28.8%
ValueCountFrequency (%)
9257113
28.8%
8260285
29.2%
475752
 
8.5%
362078
 
7.0%
291423
 
10.3%
1144570
16.2%

D19_KOSMETIK
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0301014
Minimum0
Maximum7
Zeros745836
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:40.546112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.347222584
Coefficient of variation (CV)2.278632554
Kurtosis1.541369181
Mean1.0301014
Median Absolute Deviation (MAD)0
Skewness1.86526696
Sum918048
Variance5.509453861
MonotonicityNot monotonic
2021-12-24T14:04:40.707053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0745836
83.7%
689353
 
10.0%
752858
 
5.9%
51292
 
0.1%
31263
 
0.1%
4269
 
< 0.1%
2249
 
< 0.1%
1101
 
< 0.1%
ValueCountFrequency (%)
0745836
83.7%
1101
 
< 0.1%
2249
 
< 0.1%
31263
 
0.1%
4269
 
< 0.1%
51292
 
0.1%
689353
 
10.0%
752858
 
5.9%
ValueCountFrequency (%)
752858
 
5.9%
689353
 
10.0%
51292
 
0.1%
4269
 
< 0.1%
31263
 
0.1%
2249
 
< 0.1%
1101
 
< 0.1%
0745836
83.7%

D19_LEBENSMITTEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3195245624
Minimum0
Maximum7
Zeros837914
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:40.868027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.316089627
Coefficient of variation (CV)4.118899709
Kurtosis15.11640273
Mean0.3195245624
Median Absolute Deviation (MAD)0
Skewness4.070427464
Sum284767
Variance1.732091907
MonotonicityNot monotonic
2021-12-24T14:04:41.039316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0837914
94.0%
627626
 
3.1%
310044
 
1.1%
78318
 
0.9%
55198
 
0.6%
21315
 
0.1%
4409
 
< 0.1%
1397
 
< 0.1%
ValueCountFrequency (%)
0837914
94.0%
1397
 
< 0.1%
21315
 
0.1%
310044
 
1.1%
4409
 
< 0.1%
55198
 
0.6%
627626
 
3.1%
78318
 
0.9%
ValueCountFrequency (%)
78318
 
0.9%
627626
 
3.1%
55198
 
0.6%
4409
 
< 0.1%
310044
 
1.1%
21315
 
0.1%
1397
 
< 0.1%
0837914
94.0%

D19_LETZTER_KAUF_BRANCHE
Categorical

MISSING

Distinct35
Distinct (%)< 0.1%
Missing257113
Missing (%)28.8%
Memory size6.8 MiB
D19_UNBEKANNT
195338 
D19_VERSICHERUNGEN
57734 
D19_SONSTIGE
44722 
D19_VOLLSORTIMENT
34812 
D19_SCHUHE
32578 
Other values (30)
268924 

Length

Max length22
Median length13
Mean length14.36418875
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD19_UNBEKANNT
2nd rowD19_UNBEKANNT
3rd rowD19_SCHUHE
4th rowD19_ENERGIE
5th rowD19_UNBEKANNT

Common Values

ValueCountFrequency (%)
D19_UNBEKANNT195338
21.9%
D19_VERSICHERUNGEN57734
 
6.5%
D19_SONSTIGE44722
 
5.0%
D19_VOLLSORTIMENT34812
 
3.9%
D19_SCHUHE32578
 
3.7%
D19_BUCH_CD28754
 
3.2%
D19_VERSAND_REST26034
 
2.9%
D19_DROGERIEARTIKEL24072
 
2.7%
D19_BANKEN_DIREKT23273
 
2.6%
D19_BEKLEIDUNG_REST21796
 
2.4%
Other values (25)144995
16.3%
(Missing)257113
28.8%

Length

2021-12-24T14:04:41.270725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d19_unbekannt195338
30.8%
d19_versicherungen57734
 
9.1%
d19_sonstige44722
 
7.1%
d19_vollsortiment34812
 
5.5%
d19_schuhe32578
 
5.1%
d19_buch_cd28754
 
4.5%
d19_versand_rest26034
 
4.1%
d19_drogerieartikel24072
 
3.8%
d19_banken_direkt23273
 
3.7%
d19_bekleidung_rest21796
 
3.4%
Other values (25)144995
22.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

D19_LOTTO
Real number (ℝ≥0)

MISSING
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing257113
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean1.521732891
Minimum0
Maximum7
Zeros490843
Zeros (%)55.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:41.421583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.832520981
Coefficient of variation (CV)1.861378563
Kurtosis-0.1702684246
Mean1.521732891
Median Absolute Deviation (MAD)0
Skewness1.341508541
Sum964943
Variance8.023175109
MonotonicityNot monotonic
2021-12-24T14:04:41.582529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0490843
55.1%
7113486
 
12.7%
625736
 
2.9%
52011
 
0.2%
31869
 
0.2%
478
 
< 0.1%
266
 
< 0.1%
119
 
< 0.1%
(Missing)257113
28.8%
ValueCountFrequency (%)
0490843
55.1%
119
 
< 0.1%
266
 
< 0.1%
31869
 
0.2%
478
 
< 0.1%
52011
 
0.2%
625736
 
2.9%
7113486
 
12.7%
ValueCountFrequency (%)
7113486
 
12.7%
625736
 
2.9%
52011
 
0.2%
478
 
< 0.1%
31869
 
0.2%
266
 
< 0.1%
119
 
< 0.1%
0490843
55.1%

D19_NAHRUNGSERGAENZUNG
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2445240855
Minimum0
Maximum7
Zeros852176
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:41.743781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.184654187
Coefficient of variation (CV)4.84473415
Kurtosis22.0998643
Mean0.2445240855
Median Absolute Deviation (MAD)0
Skewness4.841231013
Sum217925
Variance1.403405544
MonotonicityNot monotonic
2021-12-24T14:04:41.884621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0852176
95.6%
615778
 
1.8%
711596
 
1.3%
35768
 
0.6%
54243
 
0.5%
2679
 
0.1%
1572
 
0.1%
4409
 
< 0.1%
ValueCountFrequency (%)
0852176
95.6%
1572
 
0.1%
2679
 
0.1%
35768
 
0.6%
4409
 
< 0.1%
54243
 
0.5%
615778
 
1.8%
711596
 
1.3%
ValueCountFrequency (%)
711596
 
1.3%
615778
 
1.8%
54243
 
0.5%
4409
 
< 0.1%
35768
 
0.6%
2679
 
0.1%
1572
 
0.1%
0852176
95.6%

D19_RATGEBER
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5028303866
Minimum0
Maximum7
Zeros805071
Zeros (%)90.3%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:42.045925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.616870573
Coefficient of variation (CV)3.215538711
Kurtosis7.916111676
Mean0.5028303866
Median Absolute Deviation (MAD)0
Skewness3.088104446
Sum448133
Variance2.61427045
MonotonicityNot monotonic
2021-12-24T14:04:42.166653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0805071
90.3%
644707
 
5.0%
712287
 
1.4%
310598
 
1.2%
28334
 
0.9%
57197
 
0.8%
42136
 
0.2%
1891
 
0.1%
ValueCountFrequency (%)
0805071
90.3%
1891
 
0.1%
28334
 
0.9%
310598
 
1.2%
42136
 
0.2%
57197
 
0.8%
644707
 
5.0%
712287
 
1.4%
ValueCountFrequency (%)
712287
 
1.4%
644707
 
5.0%
57197
 
0.8%
42136
 
0.2%
310598
 
1.2%
28334
 
0.9%
1891
 
0.1%
0805071
90.3%

D19_REISEN
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.048627669
Minimum0
Maximum7
Zeros736924
Zeros (%)82.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:42.325790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.329346976
Coefficient of variation (CV)2.221328928
Kurtosis1.410457606
Mean1.048627669
Median Absolute Deviation (MAD)0
Skewness1.82118668
Sum934559
Variance5.425857335
MonotonicityNot monotonic
2021-12-24T14:04:42.488753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0736924
82.7%
694123
 
10.6%
745315
 
5.1%
35758
 
0.6%
55149
 
0.6%
22975
 
0.3%
4890
 
0.1%
187
 
< 0.1%
ValueCountFrequency (%)
0736924
82.7%
187
 
< 0.1%
22975
 
0.3%
35758
 
0.6%
4890
 
0.1%
55149
 
0.6%
694123
 
10.6%
745315
 
5.1%
ValueCountFrequency (%)
745315
 
5.1%
694123
 
10.6%
55149
 
0.6%
4890
 
0.1%
35758
 
0.6%
22975
 
0.3%
187
 
< 0.1%
0736924
82.7%

D19_SAMMELARTIKEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5946056029
Minimum0
Maximum7
Zeros802085
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:42.669814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.798291114
Coefficient of variation (CV)3.024342699
Kurtosis5.541964347
Mean0.5946056029
Median Absolute Deviation (MAD)0
Skewness2.729534579
Sum529925
Variance3.23385093
MonotonicityNot monotonic
2021-12-24T14:04:42.820724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0802085
90.0%
671297
 
8.0%
710067
 
1.1%
54072
 
0.5%
33016
 
0.3%
4470
 
0.1%
2172
 
< 0.1%
142
 
< 0.1%
ValueCountFrequency (%)
0802085
90.0%
142
 
< 0.1%
2172
 
< 0.1%
33016
 
0.3%
4470
 
0.1%
54072
 
0.5%
671297
 
8.0%
710067
 
1.1%
ValueCountFrequency (%)
710067
 
1.1%
671297
 
8.0%
54072
 
0.5%
4470
 
0.1%
33016
 
0.3%
2172
 
< 0.1%
142
 
< 0.1%
0802085
90.0%

D19_SCHUHE
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5108183043
Minimum0
Maximum7
Zeros773024
Zeros (%)86.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:43.022002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.433293004
Coefficient of variation (CV)2.80587636
Kurtosis7.427577596
Mean0.5108183043
Median Absolute Deviation (MAD)0
Skewness2.88407437
Sum455252
Variance2.054328836
MonotonicityNot monotonic
2021-12-24T14:04:43.203072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0773024
86.7%
346487
 
5.2%
620589
 
2.3%
519947
 
2.2%
217071
 
1.9%
75632
 
0.6%
14976
 
0.6%
43495
 
0.4%
ValueCountFrequency (%)
0773024
86.7%
14976
 
0.6%
217071
 
1.9%
346487
 
5.2%
43495
 
0.4%
519947
 
2.2%
620589
 
2.3%
75632
 
0.6%
ValueCountFrequency (%)
75632
 
0.6%
620589
 
2.3%
519947
 
2.2%
43495
 
0.4%
346487
 
5.2%
217071
 
1.9%
14976
 
0.6%
0773024
86.7%

D19_SONSTIGE
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.365958612
Minimum0
Maximum7
Zeros505953
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:43.404319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.861577014
Coefficient of variation (CV)1.209478898
Kurtosis-1.610906026
Mean2.365958612
Median Absolute Deviation (MAD)0
Skewness0.4965648036
Sum2108592
Variance8.18862301
MonotonicityNot monotonic
2021-12-24T14:04:43.545680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0505953
56.8%
6220478
24.7%
758373
 
6.5%
344578
 
5.0%
535028
 
3.9%
210816
 
1.2%
410204
 
1.1%
15791
 
0.6%
ValueCountFrequency (%)
0505953
56.8%
15791
 
0.6%
210816
 
1.2%
344578
 
5.0%
410204
 
1.1%
535028
 
3.9%
6220478
24.7%
758373
 
6.5%
ValueCountFrequency (%)
758373
 
6.5%
6220478
24.7%
535028
 
3.9%
410204
 
1.1%
344578
 
5.0%
210816
 
1.2%
15791
 
0.6%
0505953
56.8%

D19_SOZIALES
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing257113
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean0.657908432
Minimum0
Maximum5
Zeros505828
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:43.706597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.457773725
Coefficient of variation (CV)2.215769938
Kurtosis2.641717346
Mean0.657908432
Median Absolute Deviation (MAD)0
Skewness2.047175557
Sum417185
Variance2.125104234
MonotonicityNot monotonic
2021-12-24T14:04:43.867524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0505828
56.8%
436514
 
4.1%
530414
 
3.4%
125128
 
2.8%
321483
 
2.4%
214741
 
1.7%
(Missing)257113
28.8%
ValueCountFrequency (%)
0505828
56.8%
125128
 
2.8%
214741
 
1.7%
321483
 
2.4%
436514
 
4.1%
530414
 
3.4%
ValueCountFrequency (%)
530414
 
3.4%
436514
 
4.1%
321483
 
2.4%
214741
 
1.7%
125128
 
2.8%
0505828
56.8%

D19_TECHNIK
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.738563162
Minimum0
Maximum7
Zeros630101
Zeros (%)70.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:44.048864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.740416536
Coefficient of variation (CV)1.576253653
Kurtosis-0.9707793303
Mean1.738563162
Median Absolute Deviation (MAD)0
Skewness0.9818040267
Sum1549444
Variance7.50988279
MonotonicityNot monotonic
2021-12-24T14:04:44.229951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0630101
70.7%
6190979
 
21.4%
741174
 
4.6%
514389
 
1.6%
311064
 
1.2%
41846
 
0.2%
21163
 
0.1%
1505
 
0.1%
ValueCountFrequency (%)
0630101
70.7%
1505
 
0.1%
21163
 
0.1%
311064
 
1.2%
41846
 
0.2%
514389
 
1.6%
6190979
 
21.4%
741174
 
4.6%
ValueCountFrequency (%)
741174
 
4.6%
6190979
 
21.4%
514389
 
1.6%
41846
 
0.2%
311064
 
1.2%
21163
 
0.1%
1505
 
0.1%
0630101
70.7%

D19_TELKO_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04905629468
Minimum0
Maximum6
Zeros857990
Zeros (%)96.3%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:44.421152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2775520519
Coefficient of variation (CV)5.657827475
Kurtosis71.00257978
Mean0.04905629468
Median Absolute Deviation (MAD)0
Skewness7.350416418
Sum43720
Variance0.07703514149
MonotonicityNot monotonic
2021-12-24T14:04:44.570272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0857990
96.3%
124868
 
2.8%
26954
 
0.8%
3865
 
0.1%
4406
 
< 0.1%
5103
 
< 0.1%
635
 
< 0.1%
ValueCountFrequency (%)
0857990
96.3%
124868
 
2.8%
26954
 
0.8%
3865
 
0.1%
4406
 
< 0.1%
5103
 
< 0.1%
635
 
< 0.1%
ValueCountFrequency (%)
635
 
< 0.1%
5103
 
< 0.1%
4406
 
< 0.1%
3865
 
0.1%
26954
 
0.8%
124868
 
2.8%
0857990
96.3%

D19_TELKO_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09880377594
Minimum0
Maximum6
Zeros826208
Zeros (%)92.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:44.703322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3935872808
Coefficient of variation (CV)3.983524688
Kurtosis32.13673667
Mean0.09880377594
Median Absolute Deviation (MAD)0
Skewness5.029072305
Sum88056
Variance0.1549109476
MonotonicityNot monotonic
2021-12-24T14:04:44.824002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0826208
92.7%
146520
 
5.2%
215343
 
1.7%
32055
 
0.2%
4844
 
0.1%
5197
 
< 0.1%
654
 
< 0.1%
ValueCountFrequency (%)
0826208
92.7%
146520
 
5.2%
215343
 
1.7%
32055
 
0.2%
4844
 
0.1%
5197
 
< 0.1%
654
 
< 0.1%
ValueCountFrequency (%)
654
 
< 0.1%
5197
 
< 0.1%
4844
 
0.1%
32055
 
0.2%
215343
 
1.7%
146520
 
5.2%
0826208
92.7%

D19_TELKO_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.428727555
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:44.964739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.34428937
Coefficient of variation (CV)0.1425737845
Kurtosis11.88464681
Mean9.428727555
Median Absolute Deviation (MAD)0
Skewness-3.248079154
Sum8403080
Variance1.807113911
MonotonicityNot monotonic
2021-12-24T14:04:45.097693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10665798
74.7%
9117950
 
13.2%
842460
 
4.8%
519492
 
2.2%
718163
 
2.0%
613619
 
1.5%
45314
 
0.6%
13079
 
0.3%
22818
 
0.3%
32528
 
0.3%
ValueCountFrequency (%)
13079
 
0.3%
22818
 
0.3%
32528
 
0.3%
45314
 
0.6%
519492
 
2.2%
613619
 
1.5%
718163
 
2.0%
842460
 
4.8%
9117950
 
13.2%
10665798
74.7%
ValueCountFrequency (%)
10665798
74.7%
9117950
 
13.2%
842460
 
4.8%
718163
 
2.0%
613619
 
1.5%
519492
 
2.2%
45314
 
0.6%
32528
 
0.3%
22818
 
0.3%
13079
 
0.3%

D19_TELKO_MOBILE
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.009936929
Minimum0
Maximum7
Zeros726804
Zeros (%)81.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:45.237146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.18710219
Coefficient of variation (CV)2.165582946
Kurtosis1.300496888
Mean1.009936929
Median Absolute Deviation (MAD)0
Skewness1.782552536
Sum900077
Variance4.78341599
MonotonicityNot monotonic
2021-12-24T14:04:45.355874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0726804
81.6%
6116433
 
13.1%
316429
 
1.8%
514257
 
1.6%
78222
 
0.9%
43573
 
0.4%
23558
 
0.4%
11945
 
0.2%
ValueCountFrequency (%)
0726804
81.6%
11945
 
0.2%
23558
 
0.4%
316429
 
1.8%
43573
 
0.4%
514257
 
1.6%
6116433
 
13.1%
78222
 
0.9%
ValueCountFrequency (%)
78222
 
0.9%
6116433
 
13.1%
514257
 
1.6%
43573
 
0.4%
316429
 
1.8%
23558
 
0.4%
11945
 
0.2%
0726804
81.6%

D19_TELKO_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.828039285
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:45.504408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7458518099
Coefficient of variation (CV)0.07589019419
Kurtosis45.54768099
Mean9.828039285
Median Absolute Deviation (MAD)0
Skewness-6.138606915
Sum8758955
Variance0.5562949223
MonotonicityNot monotonic
2021-12-24T14:04:45.637170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10819114
91.9%
936707
 
4.1%
818620
 
2.1%
56309
 
0.7%
63971
 
0.4%
73590
 
0.4%
41169
 
0.1%
1682
 
0.1%
2544
 
0.1%
3515
 
0.1%
ValueCountFrequency (%)
1682
 
0.1%
2544
 
0.1%
3515
 
0.1%
41169
 
0.1%
56309
 
0.7%
63971
 
0.4%
73590
 
0.4%
818620
 
2.1%
936707
 
4.1%
10819114
91.9%
ValueCountFrequency (%)
10819114
91.9%
936707
 
4.1%
818620
 
2.1%
73590
 
0.4%
63971
 
0.4%
56309
 
0.7%
41169
 
0.1%
3515
 
0.1%
2544
 
0.1%
1682
 
0.1%

D19_TELKO_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.981780052
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:45.770069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2410353717
Coefficient of variation (CV)0.02414753385
Kurtosis470.1718655
Mean9.981780052
Median Absolute Deviation (MAD)0
Skewness-19.38108225
Sum8895972
Variance0.05809805041
MonotonicityNot monotonic
2021-12-24T14:04:45.878807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10883018
99.1%
94664
 
0.5%
81728
 
0.2%
7566
 
0.1%
5496
 
0.1%
6457
 
0.1%
4114
 
< 0.1%
168
 
< 0.1%
364
 
< 0.1%
246
 
< 0.1%
ValueCountFrequency (%)
168
 
< 0.1%
246
 
< 0.1%
364
 
< 0.1%
4114
 
< 0.1%
5496
 
0.1%
6457
 
0.1%
7566
 
0.1%
81728
 
0.2%
94664
 
0.5%
10883018
99.1%
ValueCountFrequency (%)
10883018
99.1%
94664
 
0.5%
81728
 
0.2%
7566
 
0.1%
6457
 
0.1%
5496
 
0.1%
4114
 
< 0.1%
364
 
< 0.1%
246
 
< 0.1%
168
 
< 0.1%

D19_TELKO_ONLINE_QUOTE_12
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing257113
Missing (%)28.8%
Memory size6.8 MiB
0.0
633320 
10.0
 
767
5.0
 
19
3.0
 
1
7.0
 
1

Length

Max length4
Median length3
Mean length3.001209573
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0633320
71.1%
10.0767
 
0.1%
5.019
 
< 0.1%
3.01
 
< 0.1%
7.01
 
< 0.1%
(Missing)257113
28.8%

Length

2021-12-24T14:04:46.021599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:46.132254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0633320
99.9%
10.0767
 
0.1%
5.019
 
< 0.1%
7.01
 
< 0.1%
3.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

D19_TELKO_REST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7799962075
Minimum0
Maximum7
Zeros765973
Zeros (%)85.9%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:46.253087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.969723658
Coefficient of variation (CV)2.525299019
Kurtosis2.971448125
Mean0.7799962075
Median Absolute Deviation (MAD)0
Skewness2.199683327
Sum695149
Variance3.879811288
MonotonicityNot monotonic
2021-12-24T14:04:46.403951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0765973
85.9%
688346
 
9.9%
515301
 
1.7%
311467
 
1.3%
75905
 
0.7%
42402
 
0.3%
21397
 
0.2%
1430
 
< 0.1%
ValueCountFrequency (%)
0765973
85.9%
1430
 
< 0.1%
21397
 
0.2%
311467
 
1.3%
42402
 
0.3%
515301
 
1.7%
688346
 
9.9%
75905
 
0.7%
ValueCountFrequency (%)
75905
 
0.7%
688346
 
9.9%
515301
 
1.7%
42402
 
0.3%
311467
 
1.3%
21397
 
0.2%
1430
 
< 0.1%
0765973
85.9%

D19_TIERARTIKEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2431776181
Minimum0
Maximum7
Zeros852220
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:46.583254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.170808787
Coefficient of variation (CV)4.814623963
Kurtosis21.54744599
Mean0.2431776181
Median Absolute Deviation (MAD)0
Skewness4.788953418
Sum216725
Variance1.370793217
MonotonicityNot monotonic
2021-12-24T14:04:46.716379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0852220
95.6%
620168
 
2.3%
77945
 
0.9%
36030
 
0.7%
53809
 
0.4%
2553
 
0.1%
4455
 
0.1%
141
 
< 0.1%
ValueCountFrequency (%)
0852220
95.6%
141
 
< 0.1%
2553
 
0.1%
36030
 
0.7%
4455
 
0.1%
53809
 
0.4%
620168
 
2.3%
77945
 
0.9%
ValueCountFrequency (%)
77945
 
0.9%
620168
 
2.3%
53809
 
0.4%
4455
 
0.1%
36030
 
0.7%
2553
 
0.1%
141
 
< 0.1%
0852220
95.6%

D19_VERSAND_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6046457613
Minimum0
Maximum6
Zeros637972
Zeros (%)71.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:46.875333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.150455209
Coefficient of variation (CV)1.90269292
Kurtosis3.719617491
Mean0.6046457613
Median Absolute Deviation (MAD)0
Skewness2.062303928
Sum538873
Variance1.323547188
MonotonicityNot monotonic
2021-12-24T14:04:47.008101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0637972
71.6%
196577
 
10.8%
281616
 
9.2%
334258
 
3.8%
429393
 
3.3%
59712
 
1.1%
61693
 
0.2%
ValueCountFrequency (%)
0637972
71.6%
196577
 
10.8%
281616
 
9.2%
334258
 
3.8%
429393
 
3.3%
59712
 
1.1%
61693
 
0.2%
ValueCountFrequency (%)
61693
 
0.2%
59712
 
1.1%
429393
 
3.3%
334258
 
3.8%
281616
 
9.2%
196577
 
10.8%
0637972
71.6%

D19_VERSAND_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9588676658
Minimum0
Maximum6
Zeros563818
Zeros (%)63.3%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:47.169172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.529453274
Coefficient of variation (CV)1.595061893
Kurtosis1.273680426
Mean0.9588676658
Median Absolute Deviation (MAD)0
Skewness1.530627127
Sum854563
Variance2.339227319
MonotonicityNot monotonic
2021-12-24T14:04:47.299963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0563818
63.3%
293666
 
10.5%
190253
 
10.1%
455016
 
6.2%
347832
 
5.4%
530398
 
3.4%
610238
 
1.1%
ValueCountFrequency (%)
0563818
63.3%
190253
 
10.1%
293666
 
10.5%
347832
 
5.4%
455016
 
6.2%
530398
 
3.4%
610238
 
1.1%
ValueCountFrequency (%)
610238
 
1.1%
530398
 
3.4%
455016
 
6.2%
347832
 
5.4%
293666
 
10.5%
190253
 
10.1%
0563818
63.3%

D19_VERSAND_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.717379864
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:47.440830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.989552188
Coefficient of variation (CV)0.3873791676
Kurtosis-0.339375028
Mean7.717379864
Median Absolute Deviation (MAD)1
Skewness-1.040115424
Sum6877891
Variance8.937422284
MonotonicityNot monotonic
2021-12-24T14:04:47.561533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10437886
49.1%
9100846
 
11.3%
578589
 
8.8%
153921
 
6.1%
850332
 
5.6%
240994
 
4.6%
637125
 
4.2%
434157
 
3.8%
732104
 
3.6%
325267
 
2.8%
ValueCountFrequency (%)
153921
 
6.1%
240994
 
4.6%
325267
 
2.8%
434157
 
3.8%
578589
 
8.8%
637125
 
4.2%
732104
 
3.6%
850332
 
5.6%
9100846
 
11.3%
10437886
49.1%
ValueCountFrequency (%)
10437886
49.1%
9100846
 
11.3%
850332
 
5.6%
732104
 
3.6%
637125
 
4.2%
578589
 
8.8%
434157
 
3.8%
325267
 
2.8%
240994
 
4.6%
153921
 
6.1%

D19_VERSAND_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.326817927
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:47.692203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.456007075
Coefficient of variation (CV)0.156109735
Kurtosis9.697407283
Mean9.326817927
Median Absolute Deviation (MAD)0
Skewness-2.960151515
Sum8312256
Variance2.119956602
MonotonicityNot monotonic
2021-12-24T14:04:48.114554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10634233
71.2%
9124063
 
13.9%
857907
 
6.5%
522691
 
2.5%
719855
 
2.2%
615543
 
1.7%
45363
 
0.6%
24845
 
0.5%
13450
 
0.4%
33271
 
0.4%
ValueCountFrequency (%)
13450
 
0.4%
24845
 
0.5%
33271
 
0.4%
45363
 
0.6%
522691
 
2.5%
615543
 
1.7%
719855
 
2.2%
857907
 
6.5%
9124063
 
13.9%
10634233
71.2%
ValueCountFrequency (%)
10634233
71.2%
9124063
 
13.9%
857907
 
6.5%
719855
 
2.2%
615543
 
1.7%
522691
 
2.5%
45363
 
0.6%
33271
 
0.4%
24845
 
0.5%
13450
 
0.4%

D19_VERSAND_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.942473303
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:48.275423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.942631168
Coefficient of variation (CV)0.3704930511
Kurtosis-0.03562902018
Mean7.942473303
Median Absolute Deviation (MAD)0
Skewness-1.181770484
Sum7078499
Variance8.659078189
MonotonicityNot monotonic
2021-12-24T14:04:48.416246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10494464
55.5%
982541
 
9.3%
570781
 
7.9%
149813
 
5.6%
838330
 
4.3%
237014
 
4.2%
634143
 
3.8%
431525
 
3.5%
729454
 
3.3%
323156
 
2.6%
ValueCountFrequency (%)
149813
 
5.6%
237014
 
4.2%
323156
 
2.6%
431525
 
3.5%
570781
 
7.9%
634143
 
3.8%
729454
 
3.3%
838330
 
4.3%
982541
 
9.3%
10494464
55.5%
ValueCountFrequency (%)
10494464
55.5%
982541
 
9.3%
838330
 
4.3%
729454
 
3.3%
634143
 
3.8%
570781
 
7.9%
431525
 
3.5%
323156
 
2.6%
237014
 
4.2%
149813
 
5.6%

D19_VERSAND_ONLINE_QUOTE_12
Real number (ℝ≥0)

MISSING
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing257113
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean3.244431548
Minimum0
Maximum10
Zeros417367
Zeros (%)46.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:48.567150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)10

Descriptive statistics

Standard deviation4.586604267
Coefficient of variation (CV)1.413685017
Kurtosis-1.411343211
Mean3.244431548
Median Absolute Deviation (MAD)0
Skewness0.7413724714
Sum2057320
Variance21.0369387
MonotonicityNot monotonic
2021-12-24T14:04:48.717885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0417367
46.8%
10187652
21.1%
58034
 
0.9%
86419
 
0.7%
74920
 
0.6%
93931
 
0.4%
32653
 
0.3%
61080
 
0.1%
2751
 
0.1%
4739
 
0.1%
(Missing)257113
28.8%
ValueCountFrequency (%)
0417367
46.8%
1562
 
0.1%
2751
 
0.1%
32653
 
0.3%
4739
 
0.1%
58034
 
0.9%
61080
 
0.1%
74920
 
0.6%
86419
 
0.7%
93931
 
0.4%
ValueCountFrequency (%)
10187652
21.1%
93931
 
0.4%
86419
 
0.7%
74920
 
0.6%
61080
 
0.1%
58034
 
0.9%
4739
 
0.1%
32653
 
0.3%
2751
 
0.1%
1562
 
0.1%

D19_VERSAND_REST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.829549573
Minimum0
Maximum7
Zeros734442
Zeros (%)82.4%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:48.868739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.912499309
Coefficient of variation (CV)2.305467173
Kurtosis2.63734736
Mean0.829549573
Median Absolute Deviation (MAD)0
Skewness2.070417007
Sum739312
Variance3.657653605
MonotonicityNot monotonic
2021-12-24T14:04:49.009641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0734442
82.4%
669248
 
7.8%
339251
 
4.4%
525227
 
2.8%
27560
 
0.8%
75770
 
0.6%
44901
 
0.5%
14822
 
0.5%
ValueCountFrequency (%)
0734442
82.4%
14822
 
0.5%
27560
 
0.8%
339251
 
4.4%
44901
 
0.5%
525227
 
2.8%
669248
 
7.8%
75770
 
0.6%
ValueCountFrequency (%)
75770
 
0.6%
669248
 
7.8%
525227
 
2.8%
44901
 
0.5%
339251
 
4.4%
27560
 
0.8%
14822
 
0.5%
0734442
82.4%

D19_VERSI_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1135969642
Minimum0
Maximum6
Zeros821289
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:49.170546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4348766063
Coefficient of variation (CV)3.828241445
Kurtosis25.30603907
Mean0.1135969642
Median Absolute Deviation (MAD)0
Skewness4.631649786
Sum101240
Variance0.1891176627
MonotonicityNot monotonic
2021-12-24T14:04:49.319302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0821289
92.2%
144933
 
5.0%
220273
 
2.3%
33335
 
0.4%
41210
 
0.1%
5170
 
< 0.1%
611
 
< 0.1%
ValueCountFrequency (%)
0821289
92.2%
144933
 
5.0%
220273
 
2.3%
33335
 
0.4%
41210
 
0.1%
5170
 
< 0.1%
611
 
< 0.1%
ValueCountFrequency (%)
611
 
< 0.1%
5170
 
< 0.1%
41210
 
0.1%
33335
 
0.4%
220273
 
2.3%
144933
 
5.0%
0821289
92.2%

D19_VERSI_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2069980398
Minimum0
Maximum6
Zeros777037
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:49.472248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6170164979
Coefficient of variation (CV)2.980784256
Kurtosis14.56588033
Mean0.2069980398
Median Absolute Deviation (MAD)0
Skewness3.578597394
Sum184481
Variance0.3807093587
MonotonicityNot monotonic
2021-12-24T14:04:49.623089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0777037
87.2%
163340
 
7.1%
237144
 
4.2%
38848
 
1.0%
44048
 
0.5%
5707
 
0.1%
697
 
< 0.1%
ValueCountFrequency (%)
0777037
87.2%
163340
 
7.1%
237144
 
4.2%
38848
 
1.0%
44048
 
0.5%
5707
 
0.1%
697
 
< 0.1%
ValueCountFrequency (%)
697
 
< 0.1%
5707
 
0.1%
44048
 
0.5%
38848
 
1.0%
237144
 
4.2%
163340
 
7.1%
0777037
87.2%

D19_VERSI_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.142562844
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:49.794026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q19
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.911185722
Coefficient of variation (CV)0.2090426672
Kurtosis6.061957322
Mean9.142562844
Median Absolute Deviation (MAD)0
Skewness-2.571540321
Sum8148044
Variance3.652630863
MonotonicityNot monotonic
2021-12-24T14:04:49.924724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10660478
74.1%
980231
 
9.0%
836328
 
4.1%
529040
 
3.3%
623236
 
2.6%
721016
 
2.4%
215916
 
1.8%
49593
 
1.1%
18760
 
1.0%
36623
 
0.7%
ValueCountFrequency (%)
18760
 
1.0%
215916
 
1.8%
36623
 
0.7%
49593
 
1.1%
529040
 
3.3%
623236
 
2.6%
721016
 
2.4%
836328
 
4.1%
980231
 
9.0%
10660478
74.1%
ValueCountFrequency (%)
10660478
74.1%
980231
 
9.0%
836328
 
4.1%
721016
 
2.4%
623236
 
2.6%
529040
 
3.3%
49593
 
1.1%
36623
 
0.7%
215916
 
1.8%
18760
 
1.0%

D19_VERSI_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.922648816
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:50.095726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5026652349
Coefficient of variation (CV)0.050658372
Kurtosis97.48171996
Mean9.922648816
Median Absolute Deviation (MAD)0
Skewness-9.035337778
Sum8843273
Variance0.2526723384
MonotonicityNot monotonic
2021-12-24T14:04:50.236504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10858212
96.3%
917992
 
2.0%
87013
 
0.8%
53368
 
0.4%
71911
 
0.2%
61586
 
0.2%
4544
 
0.1%
2207
 
< 0.1%
3205
 
< 0.1%
1183
 
< 0.1%
ValueCountFrequency (%)
1183
 
< 0.1%
2207
 
< 0.1%
3205
 
< 0.1%
4544
 
0.1%
53368
 
0.4%
61586
 
0.2%
71911
 
0.2%
87013
 
0.8%
917992
 
2.0%
10858212
96.3%
ValueCountFrequency (%)
10858212
96.3%
917992
 
2.0%
87013
 
0.8%
71911
 
0.2%
61586
 
0.2%
53368
 
0.4%
4544
 
0.1%
3205
 
< 0.1%
2207
 
< 0.1%
1183
 
< 0.1%

D19_VERSI_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.976711725
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:50.395342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3111910919
Coefficient of variation (CV)0.0311917494
Kurtosis330.6294738
Mean9.976711725
Median Absolute Deviation (MAD)0
Skewness-16.95221773
Sum8891455
Variance0.0968398957
MonotonicityNot monotonic
2021-12-24T14:04:50.517684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10883826
99.2%
92766
 
0.3%
81254
 
0.1%
71045
 
0.1%
5982
 
0.1%
6684
 
0.1%
4292
 
< 0.1%
2136
 
< 0.1%
3132
 
< 0.1%
1104
 
< 0.1%
ValueCountFrequency (%)
1104
 
< 0.1%
2136
 
< 0.1%
3132
 
< 0.1%
4292
 
< 0.1%
5982
 
0.1%
6684
 
0.1%
71045
 
0.1%
81254
 
0.1%
92766
 
0.3%
10883826
99.2%
ValueCountFrequency (%)
10883826
99.2%
92766
 
0.3%
81254
 
0.1%
71045
 
0.1%
6684
 
0.1%
5982
 
0.1%
4292
 
< 0.1%
3132
 
< 0.1%
2136
 
< 0.1%
1104
 
< 0.1%

D19_VERSI_ONLINE_QUOTE_12
Real number (ℝ≥0)

MISSING
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing257113
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean0.02522756376
Minimum0
Maximum10
Zeros632462
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:50.668489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4980100736
Coefficient of variation (CV)19.74071212
Kurtosis392.4967988
Mean0.02522756376
Median Absolute Deviation (MAD)0
Skewness19.82712069
Sum15997
Variance0.2480140334
MonotonicityNot monotonic
2021-12-24T14:04:50.819318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0632462
71.0%
101548
 
0.2%
570
 
< 0.1%
711
 
< 0.1%
39
 
< 0.1%
86
 
< 0.1%
61
 
< 0.1%
91
 
< 0.1%
(Missing)257113
28.8%
ValueCountFrequency (%)
0632462
71.0%
39
 
< 0.1%
570
 
< 0.1%
61
 
< 0.1%
711
 
< 0.1%
86
 
< 0.1%
91
 
< 0.1%
101548
 
0.2%
ValueCountFrequency (%)
101548
 
0.2%
91
 
< 0.1%
86
 
< 0.1%
711
 
< 0.1%
61
 
< 0.1%
570
 
< 0.1%
39
 
< 0.1%
0632462
71.0%

D19_VERSICHERUNGEN
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.259786293
Minimum0
Maximum7
Zeros654664
Zeros (%)73.5%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:50.988773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.253954199
Coefficient of variation (CV)1.789155995
Kurtosis0.2188703704
Mean1.259786293
Median Absolute Deviation (MAD)0
Skewness1.405449114
Sum1122748
Variance5.08030953
MonotonicityNot monotonic
2021-12-24T14:04:51.119488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0654664
73.5%
6116559
 
13.1%
344933
 
5.0%
530998
 
3.5%
214892
 
1.7%
413254
 
1.5%
110107
 
1.1%
75814
 
0.7%
ValueCountFrequency (%)
0654664
73.5%
110107
 
1.1%
214892
 
1.7%
344933
 
5.0%
413254
 
1.5%
530998
 
3.5%
6116559
 
13.1%
75814
 
0.7%
ValueCountFrequency (%)
75814
 
0.7%
6116559
 
13.1%
530998
 
3.5%
413254
 
1.5%
344933
 
5.0%
214892
 
1.7%
110107
 
1.1%
0654664
73.5%

D19_VOLLSORTIMENT
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.728497197
Minimum0
Maximum7
Zeros600002
Zeros (%)67.3%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:51.252201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.605193077
Coefficient of variation (CV)1.507201216
Kurtosis-0.9302481604
Mean1.728497197
Median Absolute Deviation (MAD)0
Skewness0.9580696057
Sum1540473
Variance6.787030967
MonotonicityNot monotonic
2021-12-24T14:04:51.393021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0600002
67.3%
6173626
 
19.5%
344589
 
5.0%
531303
 
3.5%
723231
 
2.6%
28250
 
0.9%
46366
 
0.7%
13854
 
0.4%
ValueCountFrequency (%)
0600002
67.3%
13854
 
0.4%
28250
 
0.9%
344589
 
5.0%
46366
 
0.7%
531303
 
3.5%
6173626
 
19.5%
723231
 
2.6%
ValueCountFrequency (%)
723231
 
2.6%
6173626
 
19.5%
531303
 
3.5%
46366
 
0.7%
344589
 
5.0%
28250
 
0.9%
13854
 
0.4%
0600002
67.3%

D19_WEIN_FEINKOST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3775146681
Minimum0
Maximum7
Zeros836142
Zeros (%)93.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:51.564121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.493150872
Coefficient of variation (CV)3.955212865
Kurtosis12.5432718
Mean0.3775146681
Median Absolute Deviation (MAD)0
Skewness3.781988175
Sum336449
Variance2.229499526
MonotonicityNot monotonic
2021-12-24T14:04:51.714808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0836142
93.8%
627556
 
3.1%
720665
 
2.3%
33460
 
0.4%
52952
 
0.3%
4231
 
< 0.1%
2179
 
< 0.1%
136
 
< 0.1%
ValueCountFrequency (%)
0836142
93.8%
136
 
< 0.1%
2179
 
< 0.1%
33460
 
0.4%
4231
 
< 0.1%
52952
 
0.3%
627556
 
3.1%
720665
 
2.3%
ValueCountFrequency (%)
720665
 
2.3%
627556
 
3.1%
52952
 
0.3%
4231
 
< 0.1%
33460
 
0.4%
2179
 
< 0.1%
136
 
< 0.1%
0836142
93.8%

DSL_FLAG
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing93148
Missing (%)10.5%
Memory size6.8 MiB
1.0
772388 
0.0
 
25685

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0772388
86.7%
0.025685
 
2.9%
(Missing)93148
 
10.5%

Length

2021-12-24T14:04:51.915913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:52.074867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0772388
96.8%
0.025685
 
3.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EINGEFUEGT_AM
Categorical

HIGH CARDINALITY
MISSING

Distinct5162
Distinct (%)0.6%
Missing93148
Missing (%)10.5%
Memory size6.8 MiB
1992-02-10 00:00:00
383738 
1992-02-12 00:00:00
192264 
1995-02-07 00:00:00
 
11181
2005-12-16 00:00:00
 
6291
2003-11-18 00:00:00
 
6050
Other values (5157)
198549 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique827 ?
Unique (%)0.1%

Sample

1st row1992-02-10 00:00:00
2nd row1992-02-12 00:00:00
3rd row1997-04-21 00:00:00
4th row1992-02-12 00:00:00
5th row1992-02-12 00:00:00

Common Values

ValueCountFrequency (%)
1992-02-10 00:00:00383738
43.1%
1992-02-12 00:00:00192264
21.6%
1995-02-07 00:00:0011181
 
1.3%
2005-12-16 00:00:006291
 
0.7%
2003-11-18 00:00:006050
 
0.7%
1993-03-01 00:00:003204
 
0.4%
2005-04-15 00:00:002343
 
0.3%
2000-05-10 00:00:002327
 
0.3%
2004-04-14 00:00:002290
 
0.3%
2005-08-23 00:00:002078
 
0.2%
Other values (5152)186307
20.9%
(Missing)93148
 
10.5%

Length

2021-12-24T14:04:52.187615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00798073
50.0%
1992-02-10383738
24.0%
1992-02-12192264
 
12.0%
1995-02-0711181
 
0.7%
2005-12-166291
 
0.4%
2003-11-186050
 
0.4%
1993-03-013204
 
0.2%
2005-04-152343
 
0.1%
2000-05-102327
 
0.1%
2004-04-142290
 
0.1%
Other values (5153)188385
 
11.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EINGEZOGENAM_HH_JAHR
Real number (ℝ≥0)

MISSING

Distinct37
Distinct (%)< 0.1%
Missing73499
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean2003.729061
Minimum1900
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:52.368593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1994
Q11997
median2003
Q32010
95-th percentile2015
Maximum2018
Range118
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.058203613
Coefficient of variation (CV)0.003522533935
Kurtosis-1.163031496
Mean2003.729061
Median Absolute Deviation (MAD)6
Skewness0.1567668637
Sum1638493335
Variance49.81823824
MonotonicityNot monotonic
2021-12-24T14:04:52.579739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1994111439
 
12.5%
199766259
 
7.4%
201545926
 
5.2%
200445103
 
5.1%
201443992
 
4.9%
200141440
 
4.6%
200834959
 
3.9%
200534714
 
3.9%
200234547
 
3.9%
201233580
 
3.8%
Other values (27)325763
36.6%
(Missing)73499
 
8.2%
ValueCountFrequency (%)
19001
 
< 0.1%
19041
 
< 0.1%
19711
 
< 0.1%
19841
 
< 0.1%
19868
 
< 0.1%
198719
 
< 0.1%
198828
 
< 0.1%
198972
 
< 0.1%
1990158
< 0.1%
1991205
< 0.1%
ValueCountFrequency (%)
2018565
 
0.1%
2017208
 
< 0.1%
201613699
 
1.5%
201545926
5.2%
201443992
4.9%
201330982
3.5%
201233580
3.8%
201125973
2.9%
201021240
2.4%
200922886
2.6%

EWDICHTE
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing93740
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean3.939172219
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:52.758671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.718995626
Coefficient of variation (CV)0.4363849891
Kurtosis-1.257676433
Mean3.939172219
Median Absolute Deviation (MAD)2
Skewness-0.3120349462
Sum3141415
Variance2.954945961
MonotonicityNot monotonic
2021-12-24T14:04:52.921568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6201009
22.6%
5161209
18.1%
2139087
15.6%
4130716
14.7%
184051
9.4%
381409
9.1%
(Missing)93740
10.5%
ValueCountFrequency (%)
184051
9.4%
2139087
15.6%
381409
9.1%
4130716
14.7%
5161209
18.1%
6201009
22.6%
ValueCountFrequency (%)
6201009
22.6%
5161209
18.1%
4130716
14.7%
381409
9.1%
2139087
15.6%
184051
9.4%

EXTSEL992
Real number (ℝ≥0)

MISSING

Distinct56
Distinct (%)< 0.1%
Missing654153
Missing (%)73.4%
Infinite0
Infinite (%)0.0%
Mean33.33839236
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:53.122714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q123
median34
Q343
95-th percentile56
Maximum56
Range55
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.53740758
Coefficient of variation (CV)0.4360560469
Kurtosis-0.614888915
Mean33.33839236
Median Absolute Deviation (MAD)11
Skewness-0.1370241946
Sum7903466
Variance211.3362192
MonotonicityNot monotonic
2021-12-24T14:04:53.331935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5619722
 
2.2%
3114987
 
1.7%
2713269
 
1.5%
3812856
 
1.4%
2312742
 
1.4%
3612059
 
1.4%
3511308
 
1.3%
559812
 
1.1%
348583
 
1.0%
506435
 
0.7%
Other values (46)115295
 
12.9%
(Missing)654153
73.4%
ValueCountFrequency (%)
11526
 
0.2%
22701
0.3%
32783
0.3%
41468
 
0.2%
51437
 
0.2%
64815
0.5%
7546
 
0.1%
8642
 
0.1%
9983
 
0.1%
10866
 
0.1%
ValueCountFrequency (%)
5619722
2.2%
559812
1.1%
544857
 
0.5%
535686
 
0.6%
521415
 
0.2%
51674
 
0.1%
506435
 
0.7%
49251
 
< 0.1%
482916
 
0.3%
471659
 
0.2%

FINANZ_ANLEGER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
5
234508 
1
210812 
2
161286 
4
143597 
3
141018 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
5234508
26.3%
1210812
23.7%
2161286
18.1%
4143597
16.1%
3141018
15.8%

Length

2021-12-24T14:04:53.523462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:53.636102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5234508
26.3%
1210812
23.7%
2161286
18.1%
4143597
16.1%
3141018
15.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_HAUSBAUER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
3
235184 
5
183918 
2
171847 
4
157168 
1
143104 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row5
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3235184
26.4%
5183918
20.6%
2171847
19.3%
4157168
17.6%
1143104
16.1%

Length

2021-12-24T14:04:53.776826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:53.887438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3235184
26.4%
5183918
20.6%
2171847
19.3%
4157168
17.6%
1143104
16.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
3
256276 
5
168863 
4
167182 
2
159313 
1
139587 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3256276
28.8%
5168863
18.9%
4167182
18.8%
2159313
17.9%
1139587
15.7%

Length

2021-12-24T14:04:54.018355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:54.118901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3256276
28.8%
5168863
18.9%
4167182
18.8%
2159313
17.9%
1139587
15.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_SPARER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
1
250213 
4
201223 
2
153051 
5
146380 
3
140354 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row4
4th row2
5th row3

Common Values

ValueCountFrequency (%)
1250213
28.1%
4201223
22.6%
2153051
17.2%
5146380
16.4%
3140354
15.7%

Length

2021-12-24T14:04:54.279782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:54.400418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1250213
28.1%
4201223
22.6%
2153051
17.2%
5146380
16.4%
3140354
15.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
1
220597 
5
200551 
2
185749 
3
170628 
4
113696 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1220597
24.8%
5200551
22.5%
2185749
20.8%
3170628
19.1%
4113696
12.8%

Length

2021-12-24T14:04:54.561279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:54.661802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1220597
24.8%
5200551
22.5%
2185749
20.8%
3170628
19.1%
4113696
12.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_VORSORGER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
5
242262 
3
229842 
4
198218 
2
116530 
1
104369 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row1
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5242262
27.2%
3229842
25.8%
4198218
22.2%
2116530
13.1%
1104369
11.7%

Length

2021-12-24T14:04:54.812691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:54.923402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5242262
27.2%
3229842
25.8%
4198218
22.2%
2116530
13.1%
1104369
11.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZTYP
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.790586173
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:55.064259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.987875961
Coefficient of variation (CV)0.5244244216
Kurtosis-1.533410771
Mean3.790586173
Median Absolute Deviation (MAD)2
Skewness-0.2397764052
Sum3378250
Variance3.951650838
MonotonicityNot monotonic
2021-12-24T14:04:55.215038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6290367
32.6%
1199572
22.4%
4130625
14.7%
2110867
 
12.4%
5106436
 
11.9%
353354
 
6.0%
ValueCountFrequency (%)
1199572
22.4%
2110867
 
12.4%
353354
 
6.0%
4130625
14.7%
5106436
 
11.9%
6290367
32.6%
ValueCountFrequency (%)
6290367
32.6%
5106436
 
11.9%
4130625
14.7%
353354
 
6.0%
2110867
 
12.4%
1199572
22.4%

FIRMENDICHTE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing93155
Missing (%)10.5%
Memory size6.8 MiB
4.0
273637 
3.0
181608 
5.0
159217 
2.0
139078 
1.0
44526 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row4.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0273637
30.7%
3.0181608
20.4%
5.0159217
17.9%
2.0139078
15.6%
1.044526
 
5.0%
(Missing)93155
 
10.5%

Length

2021-12-24T14:04:55.355771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:55.467403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0273637
34.3%
3.0181608
22.8%
5.0159217
20.0%
2.0139078
17.4%
1.044526
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GEBAEUDETYP
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing93148
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean2.798641227
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:55.597121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.65671341
Coefficient of variation (CV)0.949286884
Kurtosis-0.06997518803
Mean2.798641227
Median Absolute Deviation (MAD)0
Skewness1.256130407
Sum2233520
Variance7.058126142
MonotonicityNot monotonic
2021-12-24T14:04:55.727889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1460465
51.7%
3178668
 
20.0%
8152476
 
17.1%
24935
 
0.6%
4900
 
0.1%
6628
 
0.1%
51
 
< 0.1%
(Missing)93148
 
10.5%
ValueCountFrequency (%)
1460465
51.7%
24935
 
0.6%
3178668
 
20.0%
4900
 
0.1%
51
 
< 0.1%
6628
 
0.1%
8152476
 
17.1%
ValueCountFrequency (%)
8152476
 
17.1%
6628
 
0.1%
51
 
< 0.1%
4900
 
0.1%
3178668
 
20.0%
24935
 
0.6%
1460465
51.7%

GEBAEUDETYP_RASTER
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing93155
Missing (%)10.5%
Memory size6.8 MiB
4.0
359620 
3.0
205330 
5.0
159217 
2.0
58961 
1.0
 
14938

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row4.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0359620
40.4%
3.0205330
23.0%
5.0159217
17.9%
2.058961
 
6.6%
1.014938
 
1.7%
(Missing)93155
 
10.5%

Length

2021-12-24T14:04:55.938999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:56.059768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0359620
45.1%
3.0205330
25.7%
5.0159217
20.0%
2.058961
 
7.4%
1.014938
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GEBURTSJAHR
Real number (ℝ≥0)

ZEROS

Distinct117
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1101.178533
Minimum0
Maximum2017
Zeros392318
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:56.279020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1943
Q31970
95-th percentile1990
Maximum2017
Range2017
Interquartile range (IQR)1970

Descriptive statistics

Standard deviation976.5835513
Coefficient of variation (CV)0.88685306
Kurtosis-1.941480575
Mean1101.178533
Median Absolute Deviation (MAD)46
Skewness-0.240357039
Sum981393433
Variance953715.4326
MonotonicityNot monotonic
2021-12-24T14:04:56.512432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0392318
44.0%
196711183
 
1.3%
196511090
 
1.2%
196610933
 
1.2%
197010883
 
1.2%
196410799
 
1.2%
196810792
 
1.2%
196310513
 
1.2%
196910360
 
1.2%
198010275
 
1.2%
Other values (107)402075
45.1%
ValueCountFrequency (%)
0392318
44.0%
19004
 
< 0.1%
19021
 
< 0.1%
19045
 
< 0.1%
19058
 
< 0.1%
19067
 
< 0.1%
19074
 
< 0.1%
19087
 
< 0.1%
19097
 
< 0.1%
191041
 
< 0.1%
ValueCountFrequency (%)
2017593
0.1%
2016167
 
< 0.1%
2015257
 
< 0.1%
2014124
 
< 0.1%
2013380
< 0.1%
2012806
0.1%
2011485
0.1%
2010545
0.1%
2009559
0.1%
2008550
0.1%

GEMEINDETYP
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing97274
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean24.18674798
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:56.703534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median22
Q330
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.03785174
Coefficient of variation (CV)0.4977044351
Kurtosis-0.7288214936
Mean24.18674798
Median Absolute Deviation (MAD)10
Skewness0.6001933634
Sum19202996
Variance144.9098745
MonotonicityNot monotonic
2021-12-24T14:04:56.884835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
22151307
17.0%
11150715
16.9%
40125571
14.1%
30122406
13.7%
12120145
13.5%
2172777
8.2%
5051026
 
5.7%
(Missing)97274
10.9%
ValueCountFrequency (%)
11150715
16.9%
12120145
13.5%
2172777
8.2%
22151307
17.0%
30122406
13.7%
40125571
14.1%
5051026
 
5.7%
ValueCountFrequency (%)
5051026
 
5.7%
40125571
14.1%
30122406
13.7%
22151307
17.0%
2172777
8.2%
12120145
13.5%
11150715
16.9%

GFK_URLAUBERTYP
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean7.350304107
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:57.104118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median8
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.525723215
Coefficient of variation (CV)0.4796703869
Kurtosis-1.23285991
Mean7.350304107
Median Absolute Deviation (MAD)3
Skewness-0.2416175217
Sum6515067
Variance12.43072419
MonotonicityNot monotonic
2021-12-24T14:04:57.277175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12138545
15.5%
5120126
13.5%
10109127
12.2%
888042
9.9%
1179740
8.9%
463770
7.2%
960614
6.8%
356007
6.3%
153600
 
6.0%
246702
 
5.2%
Other values (2)70094
7.9%
ValueCountFrequency (%)
153600
6.0%
246702
 
5.2%
356007
6.3%
463770
7.2%
5120126
13.5%
627138
 
3.0%
742956
 
4.8%
888042
9.9%
960614
6.8%
10109127
12.2%
ValueCountFrequency (%)
12138545
15.5%
1179740
8.9%
10109127
12.2%
960614
6.8%
888042
9.9%
742956
 
4.8%
627138
 
3.0%
5120126
13.5%
463770
7.2%
356007
6.3%

GREEN_AVANTGARDE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
0
715996 
1
175225 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0715996
80.3%
1175225
 
19.7%

Length

2021-12-24T14:04:57.478660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:57.599522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0715996
80.3%
1175225
 
19.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HEALTH_TYP
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
3
310693 
2
306944 
1
162388 
-1
111196 

Length

Max length2
Median length1
Mean length1.124768155
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row3
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3310693
34.9%
2306944
34.4%
1162388
18.2%
-1111196
 
12.5%

Length

2021-12-24T14:04:57.740330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:58.293525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3310693
34.9%
2306944
34.4%
1273584
30.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HH_DELTA_FLAG
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing107602
Missing (%)12.1%
Memory size6.8 MiB
0.0
710942 
1.0
72677 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0710942
79.8%
1.072677
 
8.2%
(Missing)107602
 
12.1%

Length

2021-12-24T14:04:58.465612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:04:58.605476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0710942
90.7%
1.072677
 
9.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HH_EINKOMMEN_SCORE
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing18348
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean4.207243207
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:58.714054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.624057021
Coefficient of variation (CV)0.3860145327
Kurtosis-1.049195412
Mean4.207243207
Median Absolute Deviation (MAD)1
Skewness-0.4985505452
Sum3672389
Variance2.637561207
MonotonicityNot monotonic
2021-12-24T14:04:58.856912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6252775
28.4%
5201482
22.6%
2140817
15.8%
4139762
15.7%
384805
 
9.5%
153232
 
6.0%
(Missing)18348
 
2.1%
ValueCountFrequency (%)
153232
 
6.0%
2140817
15.8%
384805
 
9.5%
4139762
15.7%
5201482
22.6%
6252775
28.4%
ValueCountFrequency (%)
6252775
28.4%
5201482
22.6%
4139762
15.7%
384805
 
9.5%
2140817
15.8%
153232
 
6.0%

INNENSTADT
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)< 0.1%
Missing93740
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean4.549491461
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:59.037997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.028919204
Coefficient of variation (CV)0.4459661528
Kurtosis-0.9225382692
Mean4.549491461
Median Absolute Deviation (MAD)2
Skewness0.03835712867
Sum3628133
Variance4.116513137
MonotonicityNot monotonic
2021-12-24T14:04:59.208969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5147626
16.6%
4134067
15.0%
6111679
12.5%
2109048
12.2%
392818
10.4%
882870
9.3%
767463
7.6%
151910
 
5.8%
(Missing)93740
10.5%
ValueCountFrequency (%)
151910
 
5.8%
2109048
12.2%
392818
10.4%
4134067
15.0%
5147626
16.6%
6111679
12.5%
767463
7.6%
882870
9.3%
ValueCountFrequency (%)
882870
9.3%
767463
7.6%
6111679
12.5%
5147626
16.6%
4134067
15.0%
392818
10.4%
2109048
12.2%
151910
 
5.8%

KBA05_ALTER1
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.071317079
Minimum0
Maximum9
Zeros102789
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:59.369923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.53211977
Coefficient of variation (CV)0.7396838395
Kurtosis5.95524156
Mean2.071317079
Median Absolute Deviation (MAD)1
Skewness1.649317771
Sum1569845
Variance2.347390988
MonotonicityNot monotonic
2021-12-24T14:04:59.510791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2228625
25.7%
1167046
18.7%
3166129
18.6%
0102789
11.5%
478522
 
8.8%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
0102789
11.5%
1167046
18.7%
2228625
25.7%
3166129
18.6%
478522
 
8.8%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
478522
 
8.8%
3166129
18.6%
2228625
25.7%
1167046
18.7%
0102789
11.5%

KBA05_ALTER2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.149416082
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:59.659671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.33867585
Coefficient of variation (CV)0.4250552532
Kurtosis5.11928671
Mean3.149416082
Median Absolute Deviation (MAD)1
Skewness1.484007539
Sum2386933
Variance1.792053031
MonotonicityNot monotonic
2021-12-24T14:04:59.792449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3288107
32.3%
2165806
18.6%
4159928
17.9%
572236
 
8.1%
157034
 
6.4%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
157034
 
6.4%
2165806
18.6%
3288107
32.3%
4159928
17.9%
572236
 
8.1%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
572236
 
8.1%
4159928
17.9%
3288107
32.3%
2165806
18.6%
157034
 
6.4%

KBA05_ALTER3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.112195984
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:04:59.943364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.34970538
Coefficient of variation (CV)0.4336826429
Kurtosis5.080653676
Mean3.112195984
Median Absolute Deviation (MAD)1
Skewness1.460109044
Sum2358724
Variance1.821704612
MonotonicityNot monotonic
2021-12-24T14:05:00.082144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3292436
32.8%
2158737
17.8%
4156194
17.5%
168157
 
7.6%
567587
 
7.6%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
168157
 
7.6%
2158737
17.8%
3292436
32.8%
4156194
17.5%
567587
 
7.6%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
567587
 
7.6%
4156194
17.5%
3292436
32.8%
2158737
17.8%
168157
 
7.6%

KBA05_ALTER4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.91948906
Minimum0
Maximum9
Zeros50127
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:00.245039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.50037217
Coefficient of variation (CV)0.5139160103
Kurtosis3.754077481
Mean2.91948906
Median Absolute Deviation (MAD)1
Skewness0.8746712873
Sum2212672
Variance2.251116648
MonotonicityNot monotonic
2021-12-24T14:05:00.355685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3299085
33.6%
4144073
16.2%
2138597
15.6%
156822
 
6.4%
554407
 
6.1%
050127
 
5.6%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
050127
 
5.6%
156822
 
6.4%
2138597
15.6%
3299085
33.6%
4144073
16.2%
554407
 
6.1%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
554407
 
6.1%
4144073
16.2%
3299085
33.6%
2138597
15.6%
156822
 
6.4%
050127
 
5.6%

KBA05_ANHANG
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
1.0
323472 
0.0
266145 
3.0
81525 
2.0
72878 
9.0
 
13877

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0323472
36.3%
0.0266145
29.9%
3.081525
 
9.1%
2.072878
 
8.2%
9.013877
 
1.6%
(Missing)133324
15.0%

Length

2021-12-24T14:05:00.544984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:00.647525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0323472
42.7%
0.0266145
35.1%
3.081525
 
10.8%
2.072878
 
9.6%
9.013877
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_ANTG1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
0.0
261049 
1.0
161224 
2.0
126725 
3.0
117762 
4.0
91137 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row4.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0261049
29.3%
1.0161224
18.1%
2.0126725
14.2%
3.0117762
13.2%
4.091137
 
10.2%
(Missing)133324
15.0%

Length

2021-12-24T14:05:00.798399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:00.909139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0261049
34.4%
1.0161224
21.3%
2.0126725
16.7%
3.0117762
15.5%
4.091137
 
12.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_ANTG2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
0.0
292538 
1.0
163751 
2.0
138273 
3.0
134455 
4.0
 
28880

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row3.0
3rd row1.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0292538
32.8%
1.0163751
18.4%
2.0138273
15.5%
3.0134455
15.1%
4.028880
 
3.2%
(Missing)133324
15.0%

Length

2021-12-24T14:05:01.070120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:01.198884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0292538
38.6%
1.0163751
21.6%
2.0138273
18.2%
3.0134455
17.7%
4.028880
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_ANTG3
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
0.0
511545 
1.0
92748 
2.0
80234 
3.0
73370 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0511545
57.4%
1.092748
 
10.4%
2.080234
 
9.0%
3.073370
 
8.2%
(Missing)133324
 
15.0%

Length

2021-12-24T14:05:01.361766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:01.482557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0511545
67.5%
1.092748
 
12.2%
2.080234
 
10.6%
3.073370
 
9.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_ANTG4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
0.0
600171 
1.0
83591 
2.0
74135 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0600171
67.3%
1.083591
 
9.4%
2.074135
 
8.3%
(Missing)133324
 
15.0%

Length

2021-12-24T14:05:01.633352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:01.754049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0600171
79.2%
1.083591
 
11.0%
2.074135
 
9.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_AUTOQUOT
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.207993962
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:01.874708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.400238463
Coefficient of variation (CV)0.436484133
Kurtosis3.810047352
Mean3.207993962
Median Absolute Deviation (MAD)1
Skewness1.116063606
Sum2431329
Variance1.960667754
MonotonicityNot monotonic
2021-12-24T14:05:02.015517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3258013
29.0%
4194706
21.8%
2123320
13.8%
184157
 
9.4%
582910
 
9.3%
914791
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
184157
 
9.4%
2123320
13.8%
3258013
29.0%
4194706
21.8%
582910
 
9.3%
914791
 
1.7%
ValueCountFrequency (%)
914791
 
1.7%
582910
 
9.3%
4194706
21.8%
3258013
29.0%
2123320
13.8%
184157
 
9.4%

KBA05_BAUMAX
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean1.389551615
Minimum0
Maximum5
Zeros343200
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:02.166531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.779482931
Coefficient of variation (CV)1.280616648
Kurtosis-0.3354147408
Mean1.389551615
Median Absolute Deviation (MAD)1
Skewness1.087923604
Sum1053137
Variance3.166559503
MonotonicityNot monotonic
2021-12-24T14:05:02.307358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0343200
38.5%
1208417
23.4%
598923
 
11.1%
359955
 
6.7%
437718
 
4.2%
29684
 
1.1%
(Missing)133324
 
15.0%
ValueCountFrequency (%)
0343200
38.5%
1208417
23.4%
29684
 
1.1%
359955
 
6.7%
437718
 
4.2%
598923
 
11.1%
ValueCountFrequency (%)
598923
 
11.1%
437718
 
4.2%
359955
 
6.7%
29684
 
1.1%
1208417
23.4%
0343200
38.5%

KBA05_CCM1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.082453157
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:02.469397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.349762949
Coefficient of variation (CV)0.4378859564
Kurtosis5.202584146
Mean3.082453157
Median Absolute Deviation (MAD)1
Skewness1.50948481
Sum2336182
Variance1.821860017
MonotonicityNot monotonic
2021-12-24T14:05:02.609207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3290001
32.5%
2170860
19.2%
4148741
16.7%
167781
 
7.6%
565728
 
7.4%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
167781
 
7.6%
2170860
19.2%
3290001
32.5%
4148741
16.7%
565728
 
7.4%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
565728
 
7.4%
4148741
16.7%
3290001
32.5%
2170860
19.2%
167781
 
7.6%

KBA05_CCM2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.115361322
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:02.749973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.323140803
Coefficient of variation (CV)0.4247150383
Kurtosis5.635892433
Mean3.115361322
Median Absolute Deviation (MAD)1
Skewness1.544584328
Sum2361123
Variance1.750701586
MonotonicityNot monotonic
2021-12-24T14:05:02.890754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3301075
33.8%
4163350
18.3%
2157818
17.7%
162138
 
7.0%
558730
 
6.6%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
162138
 
7.0%
2157818
17.7%
3301075
33.8%
4163350
18.3%
558730
 
6.6%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
558730
 
6.6%
4163350
18.3%
3301075
33.8%
2157818
17.7%
162138
 
7.0%

KBA05_CCM3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.144478735
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:03.061687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.351332526
Coefficient of variation (CV)0.4297477068
Kurtosis4.903103176
Mean3.144478735
Median Absolute Deviation (MAD)1
Skewness1.410426798
Sum2383191
Variance1.826099595
MonotonicityNot monotonic
2021-12-24T14:05:03.202446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3285942
32.1%
4166348
18.7%
2154038
17.3%
570510
 
7.9%
166273
 
7.4%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
166273
 
7.4%
2154038
17.3%
3285942
32.1%
4166348
18.7%
570510
 
7.9%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
570510
 
7.9%
4166348
18.7%
3285942
32.1%
2154038
17.3%
166273
 
7.4%

KBA05_CCM4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean1.358658235
Minimum0
Maximum9
Zeros274064
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:03.353309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.621032055
Coefficient of variation (CV)1.19311245
Kurtosis7.36516988
Mean1.358658235
Median Absolute Deviation (MAD)1
Skewness2.215730085
Sum1029723
Variance2.627744924
MonotonicityNot monotonic
2021-12-24T14:05:03.502358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0274064
30.8%
1214682
24.1%
2128431
14.4%
378631
 
8.8%
447303
 
5.3%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
0274064
30.8%
1214682
24.1%
2128431
14.4%
378631
 
8.8%
447303
 
5.3%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
447303
 
5.3%
378631
 
8.8%
2128431
14.4%
1214682
24.1%
0274064
30.8%

KBA05_DIESEL
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.147869697
Minimum0
Maximum9
Zeros64600
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:03.622985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.427566055
Coefficient of variation (CV)0.6646427651
Kurtosis8.147408728
Mean2.147869697
Median Absolute Deviation (MAD)1
Skewness1.992599193
Sum1627864
Variance2.037944841
MonotonicityNot monotonic
2021-12-24T14:05:03.735723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2294616
33.1%
3163675
18.4%
1155449
17.4%
464771
 
7.3%
064600
 
7.2%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
064600
 
7.2%
1155449
17.4%
2294616
33.1%
3163675
18.4%
464771
 
7.3%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
464771
 
7.3%
3163675
18.4%
2294616
33.1%
1155449
17.4%
064600
 
7.2%

KBA05_FRAU
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.110022866
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:03.884565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.349793845
Coefficient of variation (CV)0.4340141225
Kurtosis5.103451794
Mean3.110022866
Median Absolute Deviation (MAD)1
Skewness1.468568012
Sum2357077
Variance1.821943424
MonotonicityNot monotonic
2021-12-24T14:05:04.015335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3303220
34.0%
2153893
17.3%
4146721
16.5%
570099
 
7.9%
169178
 
7.8%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
169178
 
7.8%
2153893
17.3%
3303220
34.0%
4146721
16.5%
570099
 
7.9%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
570099
 
7.9%
4146721
16.5%
3303220
34.0%
2153893
17.3%
169178
 
7.8%

KBA05_GBZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
3.0
197833 
5.0
158971 
4.0
155301 
2.0
138528 
1.0
107264 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0197833
22.2%
5.0158971
17.8%
4.0155301
17.4%
2.0138528
15.5%
1.0107264
12.0%
(Missing)133324
15.0%

Length

2021-12-24T14:05:04.206354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:04.329059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0197833
26.1%
5.0158971
21.0%
4.0155301
20.5%
2.0138528
18.3%
1.0107264
14.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_HERST1
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.47634705
Minimum0
Maximum9
Zeros75567
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:04.460871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.635225823
Coefficient of variation (CV)0.6603379051
Kurtosis3.09938565
Mean2.47634705
Median Absolute Deviation (MAD)1
Skewness1.15420156
Sum1876816
Variance2.673963493
MonotonicityNot monotonic
2021-12-24T14:05:04.590697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2225687
25.3%
3177138
19.9%
1118781
13.3%
487517
 
9.8%
075567
 
8.5%
558421
 
6.6%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
075567
 
8.5%
1118781
13.3%
2225687
25.3%
3177138
19.9%
487517
 
9.8%
558421
 
6.6%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
558421
 
6.6%
487517
 
9.8%
3177138
19.9%
2225687
25.3%
1118781
13.3%
075567
 
8.5%

KBA05_HERST2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.103159136
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:04.721378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.316661495
Coefficient of variation (CV)0.4242971235
Kurtosis5.826292731
Mean3.103159136
Median Absolute Deviation (MAD)1
Skewness1.622613982
Sum2351875
Variance1.733597494
MonotonicityNot monotonic
2021-12-24T14:05:04.860160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3301932
33.9%
2172981
19.4%
4152039
17.1%
560682
 
6.8%
155477
 
6.2%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
155477
 
6.2%
2172981
19.4%
3301932
33.9%
4152039
17.1%
560682
 
6.8%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
560682
 
6.8%
4152039
17.1%
3301932
33.9%
2172981
19.4%
155477
 
6.2%

KBA05_HERST3
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.035828087
Minimum0
Maximum9
Zeros16789
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:05.023118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.394467658
Coefficient of variation (CV)0.4593368324
Kurtosis4.812019706
Mean3.035828087
Median Absolute Deviation (MAD)1
Skewness1.252406311
Sum2300845
Variance1.944540048
MonotonicityNot monotonic
2021-12-24T14:05:05.161860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3298419
33.5%
2159475
17.9%
4147284
16.5%
560821
 
6.8%
160323
 
6.8%
016789
 
1.9%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
016789
 
1.9%
160323
 
6.8%
2159475
17.9%
3298419
33.5%
4147284
16.5%
560821
 
6.8%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
560821
 
6.8%
4147284
16.5%
3298419
33.5%
2159475
17.9%
160323
 
6.8%
016789
 
1.9%

KBA05_HERST4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.955032148
Minimum0
Maximum9
Zeros30739
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:05.302628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.496686498
Coefficient of variation (CV)0.5064873824
Kurtosis3.502252877
Mean2.955032148
Median Absolute Deviation (MAD)1
Skewness0.9979404135
Sum2239610
Variance2.240070472
MonotonicityNot monotonic
2021-12-24T14:05:05.425275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3259533
29.1%
2164362
18.4%
4142648
16.0%
175131
 
8.4%
570698
 
7.9%
030739
 
3.4%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
030739
 
3.4%
175131
 
8.4%
2164362
18.4%
3259533
29.1%
4142648
16.0%
570698
 
7.9%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
570698
 
7.9%
4142648
16.0%
3259533
29.1%
2164362
18.4%
175131
 
8.4%
030739
 
3.4%

KBA05_HERST5
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.923587242
Minimum0
Maximum9
Zeros47295
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:05.576178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.539973249
Coefficient of variation (CV)0.5267409936
Kurtosis3.110544784
Mean2.923587242
Median Absolute Deviation (MAD)1
Skewness0.8401280885
Sum2215778
Variance2.371517607
MonotonicityNot monotonic
2021-12-24T14:05:05.716977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3242170
27.2%
2164321
18.4%
4159261
17.9%
565111
 
7.3%
164953
 
7.3%
047295
 
5.3%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
047295
 
5.3%
164953
 
7.3%
2164321
18.4%
3242170
27.2%
4159261
17.9%
565111
 
7.3%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
565111
 
7.3%
4159261
17.9%
3242170
27.2%
2164321
18.4%
164953
 
7.3%
047295
 
5.3%

KBA05_HERSTTEMP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing93148
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean2.836532498
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:05.867851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.491578283
Coefficient of variation (CV)0.5258456527
Kurtosis4.075225324
Mean2.836532498
Median Absolute Deviation (MAD)1
Skewness1.40012902
Sum2263760
Variance2.224805773
MonotonicityNot monotonic
2021-12-24T14:05:05.986500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3275428
30.9%
1162386
18.2%
2157856
17.7%
4120193
13.5%
565321
 
7.3%
916889
 
1.9%
(Missing)93148
 
10.5%
ValueCountFrequency (%)
1162386
18.2%
2157856
17.7%
3275428
30.9%
4120193
13.5%
565321
 
7.3%
916889
 
1.9%
ValueCountFrequency (%)
916889
 
1.9%
565321
 
7.3%
4120193
13.5%
3275428
30.9%
2157856
17.7%
1162386
18.2%

KBA05_KRSAQUOT
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.097802208
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:06.139328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.396567291
Coefficient of variation (CV)0.4508251972
Kurtosis4.262919003
Mean3.097802208
Median Absolute Deviation (MAD)1
Skewness1.319590099
Sum2347815
Variance1.950400199
MonotonicityNot monotonic
2021-12-24T14:05:06.249915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3283526
31.8%
2151394
17.0%
4143668
16.1%
183978
 
9.4%
580545
 
9.0%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
183978
 
9.4%
2151394
17.0%
3283526
31.8%
4143668
16.1%
580545
 
9.0%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
580545
 
9.0%
4143668
16.1%
3283526
31.8%
2151394
17.0%
183978
 
9.4%

KBA05_KRSHERST1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.049426241
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:06.400723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.308094572
Coefficient of variation (CV)0.428964162
Kurtosis6.300932374
Mean3.049426241
Median Absolute Deviation (MAD)1
Skewness1.671382619
Sum2311151
Variance1.71111141
MonotonicityNot monotonic
2021-12-24T14:05:06.541400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3299103
33.6%
2174764
19.6%
4161780
18.2%
163300
 
7.1%
544164
 
5.0%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
163300
 
7.1%
2174764
19.6%
3299103
33.6%
4161780
18.2%
544164
 
5.0%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
544164
 
5.0%
4161780
18.2%
3299103
33.6%
2174764
19.6%
163300
 
7.1%

KBA05_KRSHERST2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.078737612
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:06.702170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.345042929
Coefficient of variation (CV)0.4368813124
Kurtosis5.334440767
Mean3.078737612
Median Absolute Deviation (MAD)1
Skewness1.505259342
Sum2333366
Variance1.80914048
MonotonicityNot monotonic
2021-12-24T14:05:06.861162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3297896
33.4%
2160002
18.0%
4152437
17.1%
171757
 
8.1%
561019
 
6.8%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
171757
 
8.1%
2160002
18.0%
3297896
33.4%
4152437
17.1%
561019
 
6.8%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
561019
 
6.8%
4152437
17.1%
3297896
33.4%
2160002
18.0%
171757
 
8.1%

KBA05_KRSHERST3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.153801902
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:07.040903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.362344591
Coefficient of variation (CV)0.4319689799
Kurtosis4.653006626
Mean3.153801902
Median Absolute Deviation (MAD)1
Skewness1.395410837
Sum2390257
Variance1.855982783
MonotonicityNot monotonic
2021-12-24T14:05:07.203552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3293298
32.9%
2154609
17.3%
4147717
16.6%
582429
 
9.2%
165058
 
7.3%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
165058
 
7.3%
2154609
17.3%
3293298
32.9%
4147717
16.6%
582429
 
9.2%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
582429
 
9.2%
4147717
16.6%
3293298
32.9%
2154609
17.3%
165058
 
7.3%

KBA05_KRSKLEIN
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
2.0
436383 
1.0
156643 
3.0
150085 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0436383
49.0%
1.0156643
 
17.6%
3.0150085
 
16.8%
9.014786
 
1.7%
(Missing)133324
 
15.0%

Length

2021-12-24T14:05:07.398548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:07.539795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0436383
57.6%
1.0156643
 
20.7%
3.0150085
 
19.8%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_KRSOBER
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
2.0
464492 
1.0
152048 
3.0
126571 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0464492
52.1%
1.0152048
 
17.1%
3.0126571
 
14.2%
9.014786
 
1.7%
(Missing)133324
 
15.0%

Length

2021-12-24T14:05:07.716429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:07.845463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0464492
61.3%
1.0152048
 
20.1%
3.0126571
 
16.7%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_KRSVAN
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
2.0
492053 
1.0
125937 
3.0
125121 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0492053
55.2%
1.0125937
 
14.1%
3.0125121
 
14.0%
9.014786
 
1.7%
(Missing)133324
 
15.0%

Length

2021-12-24T14:05:08.012127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:08.145562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0492053
64.9%
1.0125937
 
16.6%
3.0125121
 
16.5%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_KRSZUL
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
2.0
380095 
1.0
208542 
3.0
154474 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0380095
42.6%
1.0208542
23.4%
3.0154474
17.3%
9.014786
 
1.7%
(Missing)133324
 
15.0%

Length

2021-12-24T14:05:08.311560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:08.432474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0380095
50.2%
1.0208542
27.5%
3.0154474
20.4%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_KW1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.093549651
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:08.565159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.376705634
Coefficient of variation (CV)0.4450245799
Kurtosis4.622012925
Mean3.093549651
Median Absolute Deviation (MAD)1
Skewness1.374340187
Sum2344592
Variance1.895318403
MonotonicityNot monotonic
2021-12-24T14:05:08.721971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3274856
30.8%
4160522
18.0%
2160221
18.0%
178285
 
8.8%
569227
 
7.8%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
178285
 
8.8%
2160221
18.0%
3274856
30.8%
4160522
18.0%
569227
 
7.8%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
569227
 
7.8%
4160522
18.0%
3274856
30.8%
2160221
18.0%
178285
 
8.8%

KBA05_KW2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.113673758
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:08.886448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.333745429
Coefficient of variation (CV)0.4283510519
Kurtosis5.42141859
Mean3.113673758
Median Absolute Deviation (MAD)1
Skewness1.519288668
Sum2359844
Variance1.778876871
MonotonicityNot monotonic
2021-12-24T14:05:09.013798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3306246
34.4%
2155172
17.4%
4152081
17.1%
564938
 
7.3%
164674
 
7.3%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
164674
 
7.3%
2155172
17.4%
3306246
34.4%
4152081
17.1%
564938
 
7.3%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
564938
 
7.3%
4152081
17.1%
3306246
34.4%
2155172
17.4%
164674
 
7.3%

KBA05_KW3
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean1.551242451
Minimum0
Maximum9
Zeros206843
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:09.168631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.607972827
Coefficient of variation (CV)1.036570928
Kurtosis6.73297499
Mean1.551242451
Median Absolute Deviation (MAD)1
Skewness2.051198073
Sum1175682
Variance2.585576613
MonotonicityNot monotonic
2021-12-24T14:05:09.310048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1233518
26.2%
0206843
23.2%
2160776
18.0%
380358
 
9.0%
461616
 
6.9%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
0206843
23.2%
1233518
26.2%
2160776
18.0%
380358
 
9.0%
461616
 
6.9%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
461616
 
6.9%
380358
 
9.0%
2160776
18.0%
1233518
26.2%
0206843
23.2%

KBA05_MAXAH
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.386927247
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:09.444871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.493681605
Coefficient of variation (CV)0.441013785
Kurtosis1.866738547
Mean3.386927247
Median Absolute Deviation (MAD)1
Skewness0.8931149915
Sum2566942
Variance2.231084737
MonotonicityNot monotonic
2021-12-24T14:05:09.575101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3209157
23.5%
5195036
21.9%
2185708
20.8%
4102197
11.5%
151013
 
5.7%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
151013
 
5.7%
2185708
20.8%
3209157
23.5%
4102197
11.5%
5195036
21.9%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
5195036
21.9%
4102197
11.5%
3209157
23.5%
2185708
20.8%
151013
 
5.7%

KBA05_MAXBJ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
1.0
256917 
4.0
187538 
2.0
183360 
3.0
115296 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row4.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.0256917
28.8%
4.0187538
21.0%
2.0183360
20.6%
3.0115296
12.9%
9.014786
 
1.7%
(Missing)133324
15.0%

Length

2021-12-24T14:05:09.780344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:09.908350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0256917
33.9%
4.0187538
24.7%
2.0183360
24.2%
3.0115296
15.2%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MAXHERST
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.869009905
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:10.041692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.403278056
Coefficient of variation (CV)0.4891157935
Kurtosis5.020068157
Mean2.869009905
Median Absolute Deviation (MAD)1
Skewness1.667795932
Sum2174414
Variance1.969189303
MonotonicityNot monotonic
2021-12-24T14:05:10.181101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2270729
30.4%
3209450
23.5%
4116436
13.1%
181673
 
9.2%
564823
 
7.3%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
181673
 
9.2%
2270729
30.4%
3209450
23.5%
4116436
13.1%
564823
 
7.3%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
564823
 
7.3%
4116436
13.1%
3209450
23.5%
2270729
30.4%
181673
 
9.2%

KBA05_MAXSEG
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
2.0
299180 
1.0
202835 
3.0
171954 
4.0
69142 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0299180
33.6%
1.0202835
22.8%
3.0171954
19.3%
4.069142
 
7.8%
9.014786
 
1.7%
(Missing)133324
15.0%

Length

2021-12-24T14:05:10.383739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:10.522798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0299180
39.5%
1.0202835
26.8%
3.0171954
22.7%
4.069142
 
9.1%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MAXVORB
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
2.0
323300 
3.0
240866 
1.0
178945 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0323300
36.3%
3.0240866
27.0%
1.0178945
20.1%
9.014786
 
1.7%
(Missing)133324
15.0%

Length

2021-12-24T14:05:11.212271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:11.343190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0323300
42.7%
3.0240866
31.8%
1.0178945
23.6%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MOD1
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean1.437353625
Minimum0
Maximum9
Zeros286087
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:11.483272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.643943428
Coefficient of variation (CV)1.143729282
Kurtosis6.428397969
Mean1.437353625
Median Absolute Deviation (MAD)1
Skewness1.990841241
Sum1089366
Variance2.702549996
MonotonicityNot monotonic
2021-12-24T14:05:11.626364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0286087
32.1%
2180755
20.3%
1140854
15.8%
387732
 
9.8%
447683
 
5.4%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
0286087
32.1%
1140854
15.8%
2180755
20.3%
387732
 
9.8%
447683
 
5.4%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
447683
 
5.4%
387732
 
9.8%
2180755
20.3%
1140854
15.8%
0286087
32.1%

KBA05_MOD2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.091425352
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:11.795926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.328794005
Coefficient of variation (CV)0.4298321497
Kurtosis5.625205848
Mean3.091425352
Median Absolute Deviation (MAD)1
Skewness1.551614525
Sum2342982
Variance1.765693507
MonotonicityNot monotonic
2021-12-24T14:05:11.957913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3301207
33.8%
2160999
18.1%
4157456
17.7%
165695
 
7.4%
557754
 
6.5%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
165695
 
7.4%
2160999
18.1%
3301207
33.8%
4157456
17.7%
557754
 
6.5%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
557754
 
6.5%
4157456
17.7%
3301207
33.8%
2160999
18.1%
165695
 
7.4%

KBA05_MOD3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.096056588
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:12.138824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.350524022
Coefficient of variation (CV)0.4362077964
Kurtosis5.111468739
Mean3.096056588
Median Absolute Deviation (MAD)1
Skewness1.470423137
Sum2346492
Variance1.823915134
MonotonicityNot monotonic
2021-12-24T14:05:12.304159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3276748
31.1%
2170403
19.1%
4165736
18.6%
167924
 
7.6%
562300
 
7.0%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
167924
 
7.6%
2170403
19.1%
3276748
31.1%
4165736
18.6%
562300
 
7.0%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
562300
 
7.0%
4165736
18.6%
3276748
31.1%
2170403
19.1%
167924
 
7.6%

KBA05_MOD4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.832566958
Minimum0
Maximum9
Zeros50590
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:12.488977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.612726735
Coefficient of variation (CV)0.5693516725
Kurtosis2.432672245
Mean2.832566958
Median Absolute Deviation (MAD)1
Skewness0.834866862
Sum2146794
Variance2.600887522
MonotonicityNot monotonic
2021-12-24T14:05:12.649590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3223139
25.0%
2160094
18.0%
4130801
14.7%
197881
11.0%
580606
 
9.0%
050590
 
5.7%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
050590
 
5.7%
197881
11.0%
2160094
18.0%
3223139
25.0%
4130801
14.7%
580606
 
9.0%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
580606
 
9.0%
4130801
14.7%
3223139
25.0%
2160094
18.0%
197881
11.0%
050590
 
5.7%

KBA05_MOD8
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
0.0
221889 
1.0
217315 
2.0
216657 
3.0
87250 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0221889
24.9%
1.0217315
24.4%
2.0216657
24.3%
3.087250
 
9.8%
9.014786
 
1.7%
(Missing)133324
15.0%

Length

2021-12-24T14:05:12.841541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:12.968733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0221889
29.3%
1.0217315
28.7%
2.0216657
28.6%
3.087250
 
11.5%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MODTEMP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing93148
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean3.006466827
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:13.113931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.255616012
Coefficient of variation (CV)0.4176384054
Kurtosis-0.7025907215
Mean3.006466827
Median Absolute Deviation (MAD)1
Skewness-0.1950516299
Sum2399380
Variance1.576571568
MonotonicityNot monotonic
2021-12-24T14:05:13.278472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3267178
30.0%
4226782
25.4%
1151667
17.0%
277576
 
8.7%
565321
 
7.3%
69549
 
1.1%
(Missing)93148
 
10.5%
ValueCountFrequency (%)
1151667
17.0%
277576
 
8.7%
3267178
30.0%
4226782
25.4%
565321
 
7.3%
69549
 
1.1%
ValueCountFrequency (%)
69549
 
1.1%
565321
 
7.3%
4226782
25.4%
3267178
30.0%
277576
 
8.7%
1151667
17.0%

KBA05_MOTOR
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
3.0
289858 
2.0
222119 
1.0
121085 
4.0
110049 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0289858
32.5%
2.0222119
24.9%
1.0121085
13.6%
4.0110049
 
12.3%
9.014786
 
1.7%
(Missing)133324
15.0%

Length

2021-12-24T14:05:13.460262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:13.584740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0289858
38.2%
2.0222119
29.3%
1.0121085
16.0%
4.0110049
 
14.5%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MOTRAD
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
1.0
392117 
0.0
204268 
2.0
74250 
3.0
74050 
9.0
 
13212

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row3.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0392117
44.0%
0.0204268
22.9%
2.074250
 
8.3%
3.074050
 
8.3%
9.013212
 
1.5%
(Missing)133324
 
15.0%

Length

2021-12-24T14:05:13.765568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:13.911546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0392117
51.7%
0.0204268
27.0%
2.074250
 
9.8%
3.074050
 
9.8%
9.013212
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
1.0
251176 
0.0
246416 
2.0
185910 
3.0
59609 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row2.0
3rd row1.0
4th row3.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0251176
28.2%
0.0246416
27.6%
2.0185910
20.9%
3.059609
 
6.7%
9.014786
 
1.7%
(Missing)133324
15.0%

Length

2021-12-24T14:05:14.097409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:14.261635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0251176
33.1%
0.0246416
32.5%
2.0185910
24.5%
3.059609
 
7.9%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG10
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.005177485
Minimum0
Maximum9
Zeros111769
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:14.414256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.508828219
Coefficient of variation (CV)0.7524661684
Kurtosis6.803474938
Mean2.005177485
Median Absolute Deviation (MAD)1
Skewness1.788125524
Sum1519718
Variance2.276562594
MonotonicityNot monotonic
2021-12-24T14:05:14.580588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2267744
30.0%
1151524
17.0%
3148664
16.7%
0111769
12.5%
463410
 
7.1%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
0111769
12.5%
1151524
17.0%
2267744
30.0%
3148664
16.7%
463410
 
7.1%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
463410
 
7.1%
3148664
16.7%
2267744
30.0%
1151524
17.0%
0111769
12.5%

KBA05_SEG2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.098729775
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:14.757282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.332420964
Coefficient of variation (CV)0.4299894023
Kurtosis5.51844035
Mean3.098729775
Median Absolute Deviation (MAD)1
Skewness1.508051417
Sum2348518
Variance1.775345624
MonotonicityNot monotonic
2021-12-24T14:05:14.919109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3300423
33.7%
4164242
18.4%
2152469
17.1%
169404
 
7.8%
556573
 
6.3%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
169404
 
7.8%
2152469
17.1%
3300423
33.7%
4164242
18.4%
556573
 
6.3%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
556573
 
6.3%
4164242
18.4%
3300423
33.7%
2152469
17.1%
169404
 
7.8%

KBA05_SEG3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.086683283
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:15.122583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.343910335
Coefficient of variation (CV)0.4353897735
Kurtosis5.272531063
Mean3.086683283
Median Absolute Deviation (MAD)1
Skewness1.525636513
Sum2339388
Variance1.80609499
MonotonicityNot monotonic
2021-12-24T14:05:15.291267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3271266
30.4%
2184407
20.7%
4163976
18.4%
162378
 
7.0%
561084
 
6.9%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
162378
 
7.0%
2184407
20.7%
3271266
30.4%
4163976
18.4%
561084
 
6.9%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
561084
 
6.9%
4163976
18.4%
3271266
30.4%
2184407
20.7%
162378
 
7.0%

KBA05_SEG4
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.104275383
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:15.448066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.317708542
Coefficient of variation (CV)0.424481845
Kurtosis5.840678163
Mean3.104275383
Median Absolute Deviation (MAD)1
Skewness1.596864651
Sum2352721
Variance1.736355801
MonotonicityNot monotonic
2021-12-24T14:05:15.613000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3322991
36.2%
2152522
17.1%
4143664
16.1%
162174
 
7.0%
561760
 
6.9%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
162174
 
7.0%
2152522
17.1%
3322991
36.2%
4143664
16.1%
561760
 
6.9%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
561760
 
6.9%
4143664
16.1%
3322991
36.2%
2152522
17.1%
162174
 
7.0%

KBA05_SEG5
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean1.597991548
Minimum0
Maximum9
Zeros182816
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:15.818614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.567610921
Coefficient of variation (CV)0.9809882435
Kurtosis7.412300162
Mean1.597991548
Median Absolute Deviation (MAD)1
Skewness2.112108964
Sum1211113
Variance2.457404001
MonotonicityNot monotonic
2021-12-24T14:05:15.980513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1235093
26.4%
2183424
20.6%
0182816
20.5%
391014
 
10.2%
450764
 
5.7%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
0182816
20.5%
1235093
26.4%
2183424
20.6%
391014
 
10.2%
450764
 
5.7%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
450764
 
5.7%
391014
 
10.2%
2183424
20.6%
1235093
26.4%
0182816
20.5%

KBA05_SEG6
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
0.0
654630 
1.0
88481 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0654630
73.5%
1.088481
 
9.9%
9.014786
 
1.7%
(Missing)133324
 
15.0%

Length

2021-12-24T14:05:16.170855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:16.320915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0654630
86.4%
1.088481
 
11.7%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG7
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
0.0
368086 
1.0
183860 
2.0
141172 
3.0
49993 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0368086
41.3%
1.0183860
20.6%
2.0141172
 
15.8%
3.049993
 
5.6%
9.014786
 
1.7%
(Missing)133324
 
15.0%

Length

2021-12-24T14:05:16.476166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:16.624225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0368086
48.6%
1.0183860
24.3%
2.0141172
 
18.6%
3.049993
 
6.6%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG8
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
0.0
403849 
1.0
173773 
2.0
120236 
3.0
45253 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0403849
45.3%
1.0173773
19.5%
2.0120236
 
13.5%
3.045253
 
5.1%
9.014786
 
1.7%
(Missing)133324
 
15.0%

Length

2021-12-24T14:05:16.830478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:16.992521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0403849
53.3%
1.0173773
22.9%
2.0120236
 
15.9%
3.045253
 
6.0%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG9
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Memory size6.8 MiB
0.0
257693 
1.0
240744 
2.0
188097 
3.0
56577 
9.0
 
14786

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0257693
28.9%
1.0240744
27.0%
2.0188097
21.1%
3.056577
 
6.3%
9.014786
 
1.7%
(Missing)133324
15.0%

Length

2021-12-24T14:05:17.206785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:17.373439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0257693
34.0%
1.0240744
31.8%
2.0188097
24.8%
3.056577
 
7.5%
9.014786
 
2.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_VORB0
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.970043423
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:17.544477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.417498462
Coefficient of variation (CV)0.4772652317
Kurtosis4.341234679
Mean2.970043423
Median Absolute Deviation (MAD)1
Skewness1.351841153
Sum2250987
Variance2.009301891
MonotonicityNot monotonic
2021-12-24T14:05:17.714687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3243780
27.4%
2173206
19.4%
4162160
18.2%
1107076
12.0%
556889
 
6.4%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
1107076
12.0%
2173206
19.4%
3243780
27.4%
4162160
18.2%
556889
 
6.4%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
556889
 
6.4%
4162160
18.2%
3243780
27.4%
2173206
19.4%
1107076
12.0%

KBA05_VORB1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.111914944
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:17.872964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.333840253
Coefficient of variation (CV)0.4286236215
Kurtosis5.433542943
Mean3.111914944
Median Absolute Deviation (MAD)1
Skewness1.522481541
Sum2358511
Variance1.779129821
MonotonicityNot monotonic
2021-12-24T14:05:18.048070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3310192
34.8%
2153319
17.2%
4148709
16.7%
565624
 
7.4%
165267
 
7.3%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
165267
 
7.3%
2153319
17.2%
3310192
34.8%
4148709
16.7%
565624
 
7.4%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
565624
 
7.4%
4148709
16.7%
3310192
34.8%
2153319
17.2%
165267
 
7.3%

KBA05_VORB2
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.858848894
Minimum0
Maximum9
Zeros54357
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:18.254485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.616102009
Coefficient of variation (CV)0.5652981563
Kurtosis2.381512426
Mean2.858848894
Median Absolute Deviation (MAD)1
Skewness0.7994660653
Sum2166713
Variance2.611785703
MonotonicityNot monotonic
2021-12-24T14:05:18.388302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3234755
26.3%
2160742
18.0%
4120239
13.5%
588479
 
9.9%
184539
 
9.5%
054357
 
6.1%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
054357
 
6.1%
184539
 
9.5%
2160742
18.0%
3234755
26.3%
4120239
13.5%
588479
 
9.9%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
588479
 
9.9%
4120239
13.5%
3234755
26.3%
2160742
18.0%
184539
 
9.5%
054357
 
6.1%

KBA05_ZUL1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.101210323
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:18.514184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.33663879
Coefficient of variation (CV)0.431005527
Kurtosis5.410814299
Mean3.101210323
Median Absolute Deviation (MAD)1
Skewness1.508106285
Sum2350398
Variance1.786603254
MonotonicityNot monotonic
2021-12-24T14:05:18.643010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3299375
33.6%
4158290
17.8%
2156849
17.6%
167661
 
7.6%
560936
 
6.8%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
167661
 
7.6%
2156849
17.6%
3299375
33.6%
4158290
17.8%
560936
 
6.8%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
560936
 
6.8%
4158290
17.8%
3299375
33.6%
2156849
17.6%
167661
 
7.6%

KBA05_ZUL2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.105023506
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:18.767446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.34103811
Coefficient of variation (CV)0.4318930623
Kurtosis5.280084288
Mean3.105023506
Median Absolute Deviation (MAD)1
Skewness1.502697194
Sum2353288
Variance1.798383214
MonotonicityNot monotonic
2021-12-24T14:05:18.908607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3288618
32.4%
2166431
18.7%
4159876
17.9%
164734
 
7.3%
563452
 
7.1%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
164734
 
7.3%
2166431
18.7%
3288618
32.4%
4159876
17.9%
563452
 
7.1%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
563452
 
7.1%
4159876
17.9%
3288618
32.4%
2166431
18.7%
164734
 
7.3%

KBA05_ZUL3
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.782602385
Minimum0
Maximum9
Zeros71287
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:19.054009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.614415822
Coefficient of variation (CV)0.5801820016
Kurtosis2.608783159
Mean2.782602385
Median Absolute Deviation (MAD)1
Skewness0.7585536284
Sum2108926
Variance2.606338445
MonotonicityNot monotonic
2021-12-24T14:05:19.204807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3224580
25.2%
2160683
18.0%
4156551
17.6%
173877
 
8.3%
071287
 
8.0%
556133
 
6.3%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
071287
 
8.0%
173877
 
8.3%
2160683
18.0%
3224580
25.2%
4156551
17.6%
556133
 
6.3%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
556133
 
6.3%
4156551
17.6%
3224580
25.2%
2160683
18.0%
173877
 
8.3%
071287
 
8.0%

KBA05_ZUL4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.270412734
Minimum0
Maximum9
Zeros105584
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:19.389301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.733541451
Coefficient of variation (CV)0.763535821
Kurtosis2.48023893
Mean2.270412734
Median Absolute Deviation (MAD)1
Skewness1.157745692
Sum1720739
Variance3.005165962
MonotonicityNot monotonic
2021-12-24T14:05:19.538902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2183127
20.5%
1174910
19.6%
3125299
14.1%
0105584
11.8%
4100351
11.3%
553840
 
6.0%
914786
 
1.7%
(Missing)133324
15.0%
ValueCountFrequency (%)
0105584
11.8%
1174910
19.6%
2183127
20.5%
3125299
14.1%
4100351
11.3%
553840
 
6.0%
914786
 
1.7%
ValueCountFrequency (%)
914786
 
1.7%
553840
 
6.0%
4100351
11.3%
3125299
14.1%
2183127
20.5%
1174910
19.6%
0105584
11.8%

KBA13_ALTERHALTER_30
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
333405 
2.0
160653 
4.0
147128 
1.0
72911 
5.0
71324 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0333405
37.4%
2.0160653
18.0%
4.0147128
16.5%
1.072911
 
8.2%
5.071324
 
8.0%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:19.705124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:19.857408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0333405
42.4%
2.0160653
20.5%
4.0147128
18.7%
1.072911
 
9.3%
5.071324
 
9.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ALTERHALTER_45
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
305775 
4.0
161597 
2.0
150705 
5.0
97478 
1.0
69866 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0305775
34.3%
4.0161597
18.1%
2.0150705
16.9%
5.097478
 
10.9%
1.069866
 
7.8%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:20.043509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:20.176728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0305775
38.9%
4.0161597
20.6%
2.0150705
19.2%
5.097478
 
12.4%
1.069866
 
8.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ALTERHALTER_60
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
321522 
2.0
188053 
4.0
130673 
1.0
93825 
5.0
51348 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row5.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0321522
36.1%
2.0188053
21.1%
4.0130673
14.7%
1.093825
 
10.5%
5.051348
 
5.8%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:20.331206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:20.474360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0321522
40.9%
2.0188053
23.9%
4.0130673
16.6%
1.093825
 
11.9%
5.051348
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ALTERHALTER_61
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
323096 
4.0
177428 
2.0
138065 
5.0
87118 
1.0
59714 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0323096
36.3%
4.0177428
19.9%
2.0138065
15.5%
5.087118
 
9.8%
1.059714
 
6.7%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:20.665437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:20.804175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0323096
41.1%
4.0177428
22.6%
2.0138065
17.6%
5.087118
 
11.1%
1.059714
 
7.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANTG1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
2.0
299448 
3.0
219174 
1.0
200723 
4.0
58068 
0.0
 
8008

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0299448
33.6%
3.0219174
24.6%
1.0200723
22.5%
4.058068
 
6.5%
0.08008
 
0.9%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:20.977434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:21.128289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0299448
38.1%
3.0219174
27.9%
1.0200723
25.6%
4.058068
 
7.4%
0.08008
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANTG2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
325207 
2.0
207993 
4.0
182916 
1.0
58533 
0.0
 
10772

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0325207
36.5%
2.0207993
23.3%
4.0182916
20.5%
1.058533
 
6.6%
0.010772
 
1.2%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:21.309271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:21.450107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0325207
41.4%
2.0207993
26.5%
4.0182916
23.3%
1.058533
 
7.5%
0.010772
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANTG3
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
2.0
251723 
1.0
220761 
3.0
178747 
0.0
134190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0251723
28.2%
1.0220761
24.8%
3.0178747
20.1%
0.0134190
15.1%
(Missing)105800
11.9%

Length

2021-12-24T14:05:21.641426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:21.782271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0251723
32.0%
1.0220761
28.1%
3.0178747
22.8%
0.0134190
17.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANTG4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
0.0
380795 
1.0
277982 
2.0
126644 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0380795
42.7%
1.0277982
31.2%
2.0126644
 
14.2%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:21.943165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:22.073913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0380795
48.5%
1.0277982
35.4%
2.0126644
 
16.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANZAHL_PKW
Real number (ℝ≥0)

MISSING

Distinct1261
Distinct (%)0.2%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean619.7014391
Minimum0
Maximum2300
Zeros62
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:22.234873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile201
Q1384
median549
Q3778
95-th percentile1300
Maximum2300
Range2300
Interquartile range (IQR)394

Descriptive statistics

Standard deviation340.0343177
Coefficient of variation (CV)0.5487066776
Kurtosis2.0596211
Mean619.7014391
Median Absolute Deviation (MAD)189
Skewness1.263093879
Sum486726524
Variance115623.3372
MonotonicityNot monotonic
2021-12-24T14:05:22.476285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140011722
 
1.3%
15008291
 
0.9%
13006427
 
0.7%
16006135
 
0.7%
17003795
 
0.4%
18002617
 
0.3%
4171604
 
0.2%
4641604
 
0.2%
5191600
 
0.2%
5341496
 
0.2%
Other values (1251)740130
83.0%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
062
< 0.1%
18
 
< 0.1%
26
 
< 0.1%
36
 
< 0.1%
47
 
< 0.1%
57
 
< 0.1%
65
 
< 0.1%
75
 
< 0.1%
86
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
2300611
 
0.1%
2200307
 
< 0.1%
2100651
 
0.1%
20001198
 
0.1%
19001450
 
0.2%
18002617
 
0.3%
17003795
 
0.4%
16006135
0.7%
15008291
0.9%
140011722
1.3%

KBA13_AUDI
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
346606 
2.0
162879 
4.0
158506 
5.0
60877 
1.0
56553 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row5.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0346606
38.9%
2.0162879
18.3%
4.0158506
17.8%
5.060877
 
6.8%
1.056553
 
6.3%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:22.685609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:22.828424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0346606
44.1%
2.0162879
20.7%
4.0158506
20.2%
5.060877
 
7.8%
1.056553
 
7.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_AUTOQUOTE
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.782242899
Minimum0
Maximum5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:22.987440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.049191043
Coefficient of variation (CV)0.3771026044
Kurtosis-0.4172424007
Mean2.782242899
Median Absolute Deviation (MAD)1
Skewness0.06123555661
Sum2185232
Variance1.100801845
MonotonicityNot monotonic
2021-12-24T14:05:23.140320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3322478
36.2%
2186121
20.9%
4130528
14.6%
1102004
 
11.4%
544288
 
5.0%
02
 
< 0.1%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
02
 
< 0.1%
1102004
 
11.4%
2186121
20.9%
3322478
36.2%
4130528
14.6%
544288
 
5.0%
ValueCountFrequency (%)
544288
 
5.0%
4130528
14.6%
3322478
36.2%
2186121
20.9%
1102004
 
11.4%
02
 
< 0.1%

KBA13_BAUMAX
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
1.0
491118 
5.0
115476 
2.0
69249 
3.0
59060 
4.0
50518 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0491118
55.1%
5.0115476
 
13.0%
2.069249
 
7.8%
3.059060
 
6.6%
4.050518
 
5.7%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:23.321412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:23.452147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0491118
62.5%
5.0115476
 
14.7%
2.069249
 
8.8%
3.059060
 
7.5%
4.050518
 
6.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_1999
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
359190 
2.0
166296 
4.0
159513 
1.0
51058 
5.0
49364 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0359190
40.3%
2.0166296
18.7%
4.0159513
17.9%
1.051058
 
5.7%
5.049364
 
5.5%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:23.651325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:23.792111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0359190
45.7%
2.0166296
21.2%
4.0159513
20.3%
1.051058
 
6.5%
5.049364
 
6.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_2000
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
347385 
2.0
164179 
4.0
159567 
1.0
57775 
5.0
56515 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0347385
39.0%
2.0164179
18.4%
4.0159567
17.9%
1.057775
 
6.5%
5.056515
 
6.3%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:23.955069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:24.073713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0347385
44.2%
2.0164179
20.9%
4.0159567
20.3%
1.057775
 
7.4%
5.056515
 
7.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_2004
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
364930 
2.0
166228 
4.0
157705 
1.0
49522 
5.0
47036 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0364930
40.9%
2.0166228
18.7%
4.0157705
17.7%
1.049522
 
5.6%
5.047036
 
5.3%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:24.254752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:24.397517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0364930
46.5%
2.0166228
21.2%
4.0157705
20.1%
1.049522
 
6.3%
5.047036
 
6.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_2006
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
356794 
2.0
166960 
4.0
160793 
1.0
52194 
5.0
48680 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0356794
40.0%
2.0166960
18.7%
4.0160793
18.0%
1.052194
 
5.9%
5.048680
 
5.5%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:24.566534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:24.709316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0356794
45.4%
2.0166960
21.3%
4.0160793
20.5%
1.052194
 
6.6%
5.048680
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_2008
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.430025426
Minimum0
Maximum5
Zeros134372
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:24.850136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.466413072
Coefficient of variation (CV)0.6034558553
Kurtosis-0.6612136716
Mean2.430025426
Median Absolute Deviation (MAD)1
Skewness-0.2097089564
Sum1908593
Variance2.150367297
MonotonicityNot monotonic
2021-12-24T14:05:25.000959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3274885
30.8%
2170564
19.1%
0134372
15.1%
486070
 
9.7%
569750
 
7.8%
149780
 
5.6%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0134372
15.1%
149780
 
5.6%
2170564
19.1%
3274885
30.8%
486070
 
9.7%
569750
 
7.8%
ValueCountFrequency (%)
569750
 
7.8%
486070
 
9.7%
3274885
30.8%
2170564
19.1%
149780
 
5.6%
0134372
15.1%

KBA13_BJ_2009
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.453592405
Minimum0
Maximum5
Zeros101115
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:25.139707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.448965335
Coefficient of variation (CV)0.5905485086
Kurtosis-0.7593634555
Mean2.453592405
Median Absolute Deviation (MAD)1
Skewness-0.1309785037
Sum1927103
Variance2.099500543
MonotonicityNot monotonic
2021-12-24T14:05:25.262378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3286198
32.1%
1119909
13.5%
2118034
13.2%
0101115
 
11.3%
488293
 
9.9%
571872
 
8.1%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0101115
 
11.3%
1119909
13.5%
2118034
13.2%
3286198
32.1%
488293
 
9.9%
571872
 
8.1%
ValueCountFrequency (%)
571872
 
8.1%
488293
 
9.9%
3286198
32.1%
2118034
13.2%
1119909
13.5%
0101115
 
11.3%

KBA13_BMW
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
346193 
4.0
176871 
2.0
139903 
5.0
83249 
1.0
39205 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row4.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0346193
38.8%
4.0176871
19.8%
2.0139903
15.7%
5.083249
 
9.3%
1.039205
 
4.4%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:25.413177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:25.522021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0346193
44.1%
4.0176871
22.5%
2.0139903
17.8%
5.083249
 
10.6%
1.039205
 
5.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_0_1400
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.342429092
Minimum0
Maximum5
Zeros138711
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:25.634950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.445696683
Coefficient of variation (CV)0.6171784189
Kurtosis-0.6857091243
Mean2.342429092
Median Absolute Deviation (MAD)1
Skewness-0.1666203199
Sum1839793
Variance2.090038901
MonotonicityNot monotonic
2021-12-24T14:05:25.745722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3268319
30.1%
2178378
20.0%
0138711
15.6%
481885
 
9.2%
160025
 
6.7%
558103
 
6.5%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0138711
15.6%
160025
 
6.7%
2178378
20.0%
3268319
30.1%
481885
 
9.2%
558103
 
6.5%
ValueCountFrequency (%)
558103
 
6.5%
481885
 
9.2%
3268319
30.1%
2178378
20.0%
160025
 
6.7%
0138711
15.6%

KBA13_CCM_1000
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.426497382
Minimum0
Maximum5
Zeros103227
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:25.886601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.436357475
Coefficient of variation (CV)0.5919468474
Kurtosis-0.7466181288
Mean2.426497382
Median Absolute Deviation (MAD)1
Skewness-0.1360359797
Sum1905822
Variance2.063122797
MonotonicityNot monotonic
2021-12-24T14:05:26.007195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3290316
32.6%
1120040
13.5%
2119210
13.4%
0103227
 
11.6%
486726
 
9.7%
565902
 
7.4%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0103227
 
11.6%
1120040
13.5%
2119210
13.4%
3290316
32.6%
486726
 
9.7%
565902
 
7.4%
ValueCountFrequency (%)
565902
 
7.4%
486726
 
9.7%
3290316
32.6%
2119210
13.4%
1120040
13.5%
0103227
 
11.6%

KBA13_CCM_1200
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.329705979
Minimum0
Maximum5
Zeros145802
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:26.145935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.45957818
Coefficient of variation (CV)0.6265074619
Kurtosis-0.7400300325
Mean2.329705979
Median Absolute Deviation (MAD)1
Skewness-0.1848703145
Sum1829800
Variance2.130368464
MonotonicityNot monotonic
2021-12-24T14:05:26.258521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3278653
31.3%
2161690
18.1%
0145802
16.4%
481631
 
9.2%
161072
 
6.9%
556573
 
6.3%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0145802
16.4%
161072
 
6.9%
2161690
18.1%
3278653
31.3%
481631
 
9.2%
556573
 
6.3%
ValueCountFrequency (%)
556573
 
6.3%
481631
 
9.2%
3278653
31.3%
2161690
18.1%
161072
 
6.9%
0145802
16.4%

KBA13_CCM_1400
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
362764 
2.0
169640 
4.0
161205 
5.0
49030 
1.0
42782 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0362764
40.7%
2.0169640
19.0%
4.0161205
18.1%
5.049030
 
5.5%
1.042782
 
4.8%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:26.409385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:26.530115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0362764
46.2%
2.0169640
21.6%
4.0161205
20.5%
5.049030
 
6.2%
1.042782
 
5.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_1401_2500
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
359093 
2.0
174525 
4.0
157962 
1.0
60428 
5.0
 
33413

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0359093
40.3%
2.0174525
19.6%
4.0157962
17.7%
1.060428
 
6.8%
5.033413
 
3.7%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:26.681107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:26.791724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0359093
45.7%
2.0174525
22.2%
4.0157962
20.1%
1.060428
 
7.7%
5.033413
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_1500
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
1.0
287731 
4.0
206213 
3.0
156747 
5.0
68326 
2.0
66404 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0287731
32.3%
4.0206213
23.1%
3.0156747
17.6%
5.068326
 
7.7%
2.066404
 
7.5%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:26.942597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:27.577256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0287731
36.6%
4.0206213
26.3%
3.0156747
20.0%
5.068326
 
8.7%
2.066404
 
8.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_1600
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
364222 
2.0
167171 
4.0
163429 
5.0
51951 
1.0
38648 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0364222
40.9%
2.0167171
18.8%
4.0163429
18.3%
5.051951
 
5.8%
1.038648
 
4.3%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:27.746167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:27.888979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0364222
46.4%
2.0167171
21.3%
4.0163429
20.8%
5.051951
 
6.6%
1.038648
 
4.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_1800
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.364000963
Minimum0
Maximum5
Zeros137534
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:28.028013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.43666166
Coefficient of variation (CV)0.6077246512
Kurtosis-0.6393064936
Mean2.364000963
Median Absolute Deviation (MAD)1
Skewness-0.202261127
Sum1856736
Variance2.063996726
MonotonicityNot monotonic
2021-12-24T14:05:28.190992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3276578
31.0%
2179863
20.2%
0137534
15.4%
481550
 
9.2%
557795
 
6.5%
152101
 
5.8%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0137534
15.4%
152101
 
5.8%
2179863
20.2%
3276578
31.0%
481550
 
9.2%
557795
 
6.5%
ValueCountFrequency (%)
557795
 
6.5%
481550
 
9.2%
3276578
31.0%
2179863
20.2%
152101
 
5.8%
0137534
15.4%

KBA13_CCM_2000
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
363131 
4.0
170342 
2.0
160922 
5.0
57234 
1.0
 
33792

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0363131
40.7%
4.0170342
19.1%
2.0160922
18.1%
5.057234
 
6.4%
1.033792
 
3.8%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:28.361950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:28.513200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0363131
46.2%
4.0170342
21.7%
2.0160922
20.5%
5.057234
 
7.3%
1.033792
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_2500
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.482918078
Minimum0
Maximum5
Zeros95350
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:28.641914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.414942642
Coefficient of variation (CV)0.5698708525
Kurtosis-0.6372071175
Mean2.482918078
Median Absolute Deviation (MAD)1
Skewness-0.1527969444
Sum1950136
Variance2.00206268
MonotonicityNot monotonic
2021-12-24T14:05:28.804867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3283932
31.9%
2144530
16.2%
1102470
 
11.5%
095350
 
10.7%
488885
 
10.0%
570254
 
7.9%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
095350
 
10.7%
1102470
 
11.5%
2144530
16.2%
3283932
31.9%
488885
 
10.0%
570254
 
7.9%
ValueCountFrequency (%)
570254
 
7.9%
488885
 
10.0%
3283932
31.9%
2144530
16.2%
1102470
 
11.5%
095350
 
10.7%

KBA13_CCM_2501
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.512177546
Minimum0
Maximum5
Zeros93403
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:28.965790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.449422653
Coefficient of variation (CV)0.5769586849
Kurtosis-0.7464547057
Mean2.512177546
Median Absolute Deviation (MAD)1
Skewness-0.1598613993
Sum1973117
Variance2.100826027
MonotonicityNot monotonic
2021-12-24T14:05:29.136815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3294765
33.1%
1121797
13.7%
2106344
 
11.9%
093403
 
10.5%
491223
 
10.2%
577889
 
8.7%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
093403
 
10.5%
1121797
13.7%
2106344
 
11.9%
3294765
33.1%
491223
 
10.2%
577889
 
8.7%
ValueCountFrequency (%)
577889
 
8.7%
491223
 
10.2%
3294765
33.1%
2106344
 
11.9%
1121797
13.7%
093403
 
10.5%

KBA13_CCM_3000
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.583847389
Minimum0
Maximum5
Zeros56562
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:29.277590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.368104049
Coefficient of variation (CV)0.5294833026
Kurtosis-0.6566333737
Mean2.583847389
Median Absolute Deviation (MAD)1
Skewness-0.1149045324
Sum2029408
Variance1.871708689
MonotonicityNot monotonic
2021-12-24T14:05:29.428385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3308099
34.6%
1149271
16.7%
2103136
 
11.6%
492197
 
10.3%
576156
 
8.5%
056562
 
6.3%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
056562
 
6.3%
1149271
16.7%
2103136
 
11.6%
3308099
34.6%
492197
 
10.3%
576156
 
8.5%
ValueCountFrequency (%)
576156
 
8.5%
492197
 
10.3%
3308099
34.6%
2103136
 
11.6%
1149271
16.7%
056562
 
6.3%

KBA13_CCM_3001
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
1.0
338439 
4.0
215760 
3.0
147448 
5.0
83682 
2.0
 
92

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.0338439
38.0%
4.0215760
24.2%
3.0147448
16.5%
5.083682
 
9.4%
2.092
 
< 0.1%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:29.599433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:29.720077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0338439
43.1%
4.0215760
27.5%
3.0147448
18.8%
5.083682
 
10.7%
2.092
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_FAB_ASIEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
340870 
2.0
169019 
4.0
152278 
1.0
62422 
5.0
60832 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0340870
38.2%
2.0169019
19.0%
4.0152278
17.1%
1.062422
 
7.0%
5.060832
 
6.8%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:29.860825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:29.999973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0340870
43.4%
2.0169019
21.5%
4.0152278
19.4%
1.062422
 
7.9%
5.060832
 
7.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_FAB_SONSTIGE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
345124 
2.0
167481 
4.0
153466 
5.0
61004 
1.0
58346 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0345124
38.7%
2.0167481
18.8%
4.0153466
17.2%
5.061004
 
6.8%
1.058346
 
6.5%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:30.152916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:30.291671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0345124
43.9%
2.0167481
21.3%
4.0153466
19.5%
5.061004
 
7.8%
1.058346
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_FIAT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
343347 
4.0
174024 
2.0
148334 
5.0
78722 
1.0
40994 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0343347
38.5%
4.0174024
19.5%
2.0148334
16.6%
5.078722
 
8.8%
1.040994
 
4.6%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:30.515686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:30.686789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0343347
43.7%
4.0174024
22.2%
2.0148334
18.9%
5.078722
 
10.0%
1.040994
 
5.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_FORD
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
335170 
2.0
162145 
4.0
154870 
5.0
69233 
1.0
64003 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row3.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0335170
37.6%
2.0162145
18.2%
4.0154870
17.4%
5.069233
 
7.8%
1.064003
 
7.2%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:30.847668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:30.988203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0335170
42.7%
2.0162145
20.6%
4.0154870
19.7%
5.069233
 
8.8%
1.064003
 
8.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_GBZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
284563 
4.0
184003 
5.0
167473 
2.0
109422 
1.0
39960 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.0284563
31.9%
4.0184003
20.6%
5.0167473
18.8%
2.0109422
 
12.3%
1.039960
 
4.5%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:31.159474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:31.308220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0284563
36.2%
4.0184003
23.4%
5.0167473
21.3%
2.0109422
 
13.9%
1.039960
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_20
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
338233 
2.0
184872 
4.0
146416 
1.0
66025 
5.0
49875 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0338233
38.0%
2.0184872
20.7%
4.0146416
16.4%
1.066025
 
7.4%
5.049875
 
5.6%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:31.461129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:31.591768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0338233
43.1%
2.0184872
23.5%
4.0146416
18.6%
1.066025
 
8.4%
5.049875
 
6.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_25
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
341430 
2.0
165111 
4.0
144771 
1.0
72751 
5.0
61358 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0341430
38.3%
2.0165111
18.5%
4.0144771
16.2%
1.072751
 
8.2%
5.061358
 
6.9%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:31.762968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:31.903734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0341430
43.5%
2.0165111
21.0%
4.0144771
18.4%
1.072751
 
9.3%
5.061358
 
7.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_30
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
322185 
2.0
155663 
4.0
150541 
5.0
90957 
1.0
66075 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0322185
36.2%
2.0155663
17.5%
4.0150541
16.9%
5.090957
 
10.2%
1.066075
 
7.4%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:32.074818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:32.205539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0322185
41.0%
2.0155663
19.8%
4.0150541
19.2%
5.090957
 
11.6%
1.066075
 
8.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_35
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
309769 
4.0
160396 
2.0
151032 
5.0
100377 
1.0
63847 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0309769
34.8%
4.0160396
18.0%
2.0151032
16.9%
5.0100377
 
11.3%
1.063847
 
7.2%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:32.344750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:32.457359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0309769
39.4%
4.0160396
20.4%
2.0151032
19.2%
5.0100377
 
12.8%
1.063847
 
8.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_40
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
313672 
4.0
159800 
2.0
151984 
5.0
95692 
1.0
64273 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0313672
35.2%
4.0159800
17.9%
2.0151984
17.1%
5.095692
 
10.7%
1.064273
 
7.2%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:32.628448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:32.739042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0313672
39.9%
4.0159800
20.3%
2.0151984
19.4%
5.095692
 
12.2%
1.064273
 
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_45
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
318028 
4.0
160040 
2.0
158198 
5.0
79488 
1.0
69667 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.0318028
35.7%
4.0160040
18.0%
2.0158198
17.8%
5.079488
 
8.9%
1.069667
 
7.8%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:32.867845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:32.958337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0318028
40.5%
4.0160040
20.4%
2.0158198
20.1%
5.079488
 
10.1%
1.069667
 
8.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_50
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
325071 
2.0
183530 
4.0
133612 
1.0
89591 
5.0
53617 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row5.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0325071
36.5%
2.0183530
20.6%
4.0133612
15.0%
1.089591
 
10.1%
5.053617
 
6.0%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:33.081305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:33.181826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0325071
41.4%
2.0183530
23.4%
4.0133612
17.0%
1.089591
 
11.4%
5.053617
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_55
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
319411 
2.0
183685 
4.0
135541 
1.0
92743 
5.0
54041 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row5.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0319411
35.8%
2.0183685
20.6%
4.0135541
15.2%
1.092743
 
10.4%
5.054041
 
6.1%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:33.310709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:33.403531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0319411
40.7%
2.0183685
23.4%
4.0135541
17.3%
1.092743
 
11.8%
5.054041
 
6.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_60
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
321266 
2.0
172974 
4.0
140907 
1.0
88762 
5.0
61512 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0321266
36.0%
2.0172974
19.4%
4.0140907
15.8%
1.088762
 
10.0%
5.061512
 
6.9%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:33.534486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:33.633195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0321266
40.9%
2.0172974
22.0%
4.0140907
17.9%
1.088762
 
11.3%
5.061512
 
7.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_65
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
331364 
4.0
175040 
2.0
140351 
5.0
85579 
1.0
53087 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0331364
37.2%
4.0175040
19.6%
2.0140351
15.7%
5.085579
 
9.6%
1.053087
 
6.0%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:33.765936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:33.856707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0331364
42.2%
4.0175040
22.3%
2.0140351
17.9%
5.085579
 
10.9%
1.053087
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_66
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
320451 
4.0
175161 
2.0
139386 
5.0
86577 
1.0
63846 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0320451
36.0%
4.0175161
19.7%
2.0139386
15.6%
5.086577
 
9.7%
1.063846
 
7.2%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:33.977654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:34.096632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0320451
40.8%
4.0175161
22.3%
2.0139386
17.7%
5.086577
 
11.0%
1.063846
 
8.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_ASIEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
338074 
2.0
162979 
4.0
155084 
5.0
67474 
1.0
61810 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row3.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0338074
37.9%
2.0162979
18.3%
4.0155084
17.4%
5.067474
 
7.6%
1.061810
 
6.9%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:34.227497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:34.338286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0338074
43.0%
2.0162979
20.8%
4.0155084
19.7%
5.067474
 
8.6%
1.061810
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_AUDI_VW
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
336178 
2.0
172160 
4.0
149322 
1.0
72901 
5.0
54860 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row4.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0336178
37.7%
2.0172160
19.3%
4.0149322
16.8%
1.072901
 
8.2%
5.054860
 
6.2%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:34.450920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:34.541601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0336178
42.8%
2.0172160
21.9%
4.0149322
19.0%
1.072901
 
9.3%
5.054860
 
7.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_BMW_BENZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
339754 
4.0
180052 
2.0
133074 
5.0
86958 
1.0
45583 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0339754
38.1%
4.0180052
20.2%
2.0133074
 
14.9%
5.086958
 
9.8%
1.045583
 
5.1%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:34.672673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:34.763178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0339754
43.3%
4.0180052
22.9%
2.0133074
 
16.9%
5.086958
 
11.1%
1.045583
 
5.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_EUROPA
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
341097 
4.0
170642 
2.0
151037 
5.0
72872 
1.0
49773 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0341097
38.3%
4.0170642
19.1%
2.0151037
16.9%
5.072872
 
8.2%
1.049773
 
5.6%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:34.904299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:35.014976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0341097
43.4%
4.0170642
21.7%
2.0151037
19.2%
5.072872
 
9.3%
1.049773
 
6.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_FORD_OPEL
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
326805 
2.0
164003 
4.0
154044 
1.0
74276 
5.0
66293 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0326805
36.7%
2.0164003
18.4%
4.0154044
17.3%
1.074276
 
8.3%
5.066293
 
7.4%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:35.146062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:35.256990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0326805
41.6%
2.0164003
20.9%
4.0154044
19.6%
1.074276
 
9.5%
5.066293
 
8.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_SONST
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
345124 
2.0
167481 
4.0
153466 
5.0
61004 
1.0
58346 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0345124
38.7%
2.0167481
18.8%
4.0153466
17.2%
5.061004
 
6.8%
1.058346
 
6.5%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:35.418341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:35.517026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0345124
43.9%
2.0167481
21.3%
4.0153466
19.5%
5.061004
 
7.8%
1.058346
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HHZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
319964 
4.0
212119 
5.0
168143 
2.0
72085 
1.0
 
13110

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row3.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.0319964
35.9%
4.0212119
23.8%
5.0168143
18.9%
2.072085
 
8.1%
1.013110
 
1.5%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:35.637836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:35.720563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0319964
40.7%
4.0212119
27.0%
5.0168143
21.4%
2.072085
 
9.2%
1.013110
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_0_140
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.329368581
Minimum0
Maximum5
Zeros96010
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:35.821667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.529004403
Coefficient of variation (CV)0.6564029479
Kurtosis-1.135330441
Mean2.329368581
Median Absolute Deviation (MAD)2
Skewness0.0449073815
Sum1829535
Variance2.337854465
MonotonicityNot monotonic
2021-12-24T14:05:35.920188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3283566
31.8%
1234424
26.3%
096010
 
10.8%
492670
 
10.4%
572077
 
8.1%
26674
 
0.7%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
096010
 
10.8%
1234424
26.3%
26674
 
0.7%
3283566
31.8%
492670
 
10.4%
572077
 
8.1%
ValueCountFrequency (%)
572077
 
8.1%
492670
 
10.4%
3283566
31.8%
26674
 
0.7%
1234424
26.3%
096010
 
10.8%

KBA13_KMH_110
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
1.0
627623 
3.0
94175 
2.0
63623 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0627623
70.4%
3.094175
 
10.6%
2.063623
 
7.1%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:36.033421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:36.114427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0627623
79.9%
3.094175
 
12.0%
2.063623
 
8.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_140
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
1.0
249773 
4.0
202067 
3.0
167221 
2.0
91648 
5.0
74712 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row5.0
4th row5.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0249773
28.0%
4.0202067
22.7%
3.0167221
18.8%
2.091648
 
10.3%
5.074712
 
8.4%
(Missing)105800
11.9%

Length

2021-12-24T14:05:36.215258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:36.306118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0249773
31.8%
4.0202067
25.7%
3.0167221
21.3%
2.091648
 
11.7%
5.074712
 
9.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_140_210
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
361405 
2.0
179354 
4.0
133192 
1.0
73767 
5.0
37703 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0361405
40.6%
2.0179354
20.1%
4.0133192
 
14.9%
1.073767
 
8.3%
5.037703
 
4.2%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:36.437317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:36.546341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0361405
46.0%
2.0179354
22.8%
4.0133192
 
17.0%
1.073767
 
9.4%
5.037703
 
4.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_180
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
355363 
2.0
170291 
4.0
155595 
1.0
61574 
5.0
42598 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0355363
39.9%
2.0170291
19.1%
4.0155595
17.5%
1.061574
 
6.9%
5.042598
 
4.8%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:36.679196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:36.779741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0355363
45.2%
2.0170291
21.7%
4.0155595
19.8%
1.061574
 
7.8%
5.042598
 
5.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_210
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
361259 
4.0
164843 
2.0
161113 
5.0
55495 
1.0
42711 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row2.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0361259
40.5%
4.0164843
18.5%
2.0161113
18.1%
5.055495
 
6.2%
1.042711
 
4.8%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:36.898643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:36.991185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0361259
46.0%
4.0164843
21.0%
2.0161113
20.5%
5.055495
 
7.1%
1.042711
 
5.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_211
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.457977314
Minimum0
Maximum5
Zeros139463
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:37.111951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4916275
Coefficient of variation (CV)0.6068516139
Kurtosis-0.691175133
Mean2.457977314
Median Absolute Deviation (MAD)1
Skewness-0.232285257
Sum1930547
Variance2.224952599
MonotonicityNot monotonic
2021-12-24T14:05:37.212583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3277452
31.1%
2162264
18.2%
0139463
15.6%
488043
 
9.9%
575823
 
8.5%
142376
 
4.8%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0139463
15.6%
142376
 
4.8%
2162264
18.2%
3277452
31.1%
488043
 
9.9%
575823
 
8.5%
ValueCountFrequency (%)
575823
 
8.5%
488043
 
9.9%
3277452
31.1%
2162264
18.2%
142376
 
4.8%
0139463
15.6%

KBA13_KMH_250
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.455954195
Minimum0
Maximum5
Zeros139756
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:37.313491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.490853642
Coefficient of variation (CV)0.6070364198
Kurtosis-0.6912403951
Mean2.455954195
Median Absolute Deviation (MAD)1
Skewness-0.2340187255
Sum1928958
Variance2.222644582
MonotonicityNot monotonic
2021-12-24T14:05:37.434204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3278290
31.2%
2161652
18.1%
0139756
15.7%
488055
 
9.9%
575224
 
8.4%
142444
 
4.8%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0139756
15.7%
142444
 
4.8%
2161652
18.1%
3278290
31.2%
488055
 
9.9%
575224
 
8.4%
ValueCountFrequency (%)
575224
 
8.4%
488055
 
9.9%
3278290
31.2%
2161652
18.1%
142444
 
4.8%
0139756
15.7%

KBA13_KMH_251
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
1.0
674722 
3.0
100548 
2.0
 
10151

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0674722
75.7%
3.0100548
 
11.3%
2.010151
 
1.1%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:37.565761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:37.674546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0674722
85.9%
3.0100548
 
12.8%
2.010151
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSAQUOT
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.866368483
Minimum0
Maximum5
Zeros60
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:37.767290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.062771916
Coefficient of variation (CV)0.3707729561
Kurtosis-0.4226033777
Mean2.866368483
Median Absolute Deviation (MAD)1
Skewness0.02975234482
Sum2251306
Variance1.129484145
MonotonicityNot monotonic
2021-12-24T14:05:37.888137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3324094
36.4%
2172621
19.4%
4142200
16.0%
191812
 
10.3%
554634
 
6.1%
060
 
< 0.1%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
060
 
< 0.1%
191812
 
10.3%
2172621
19.4%
3324094
36.4%
4142200
16.0%
554634
 
6.1%
ValueCountFrequency (%)
554634
 
6.1%
4142200
16.0%
3324094
36.4%
2172621
19.4%
191812
 
10.3%
060
 
< 0.1%

KBA13_KRSHERST_AUDI_VW
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.959943775
Minimum0
Maximum5
Zeros58
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:37.988909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.01152282
Coefficient of variation (CV)0.3417371737
Kurtosis-0.296252122
Mean2.959943775
Median Absolute Deviation (MAD)1
Skewness-0.0169618502
Sum2324802
Variance1.023178416
MonotonicityNot monotonic
2021-12-24T14:05:38.097788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3337010
37.8%
2167559
18.8%
4162124
18.2%
165798
 
7.4%
552872
 
5.9%
058
 
< 0.1%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
058
 
< 0.1%
165798
 
7.4%
2167559
18.8%
3337010
37.8%
4162124
18.2%
552872
 
5.9%
ValueCountFrequency (%)
552872
 
5.9%
4162124
18.2%
3337010
37.8%
2167559
18.8%
165798
 
7.4%
058
 
< 0.1%

KBA13_KRSHERST_BMW_BENZ
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean3.083095563
Minimum0
Maximum5
Zeros58
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:38.200877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.009634881
Coefficient of variation (CV)0.3274744038
Kurtosis-0.2669559679
Mean3.083095563
Median Absolute Deviation (MAD)1
Skewness0.03062636879
Sum2421528
Variance1.019362593
MonotonicityNot monotonic
2021-12-24T14:05:38.311698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3345264
38.7%
4165044
18.5%
2153245
17.2%
574315
 
8.3%
147495
 
5.3%
058
 
< 0.1%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
058
 
< 0.1%
147495
 
5.3%
2153245
17.2%
3345264
38.7%
4165044
18.5%
574315
 
8.3%
ValueCountFrequency (%)
574315
 
8.3%
4165044
18.5%
3345264
38.7%
2153245
17.2%
147495
 
5.3%
058
 
< 0.1%

KBA13_KRSHERST_FORD_OPEL
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean3.008965892
Minimum0
Maximum5
Zeros58
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:38.422668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.040945041
Coefficient of variation (CV)0.3459477702
Kurtosis-0.3612092944
Mean3.008965892
Median Absolute Deviation (MAD)1
Skewness-0.020214452
Sum2363305
Variance1.083566579
MonotonicityNot monotonic
2021-12-24T14:05:38.523651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3329240
36.9%
4166035
18.6%
2158597
17.8%
165801
 
7.4%
565690
 
7.4%
058
 
< 0.1%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
058
 
< 0.1%
165801
 
7.4%
2158597
17.8%
3329240
36.9%
4166035
18.6%
565690
 
7.4%
ValueCountFrequency (%)
565690
 
7.4%
4166035
18.6%
3329240
36.9%
2158597
17.8%
165801
 
7.4%
058
 
< 0.1%

KBA13_KRSSEG_KLEIN
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
2.0
718366 
1.0
 
35557
3.0
 
31418
0.0
 
80

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0718366
80.6%
1.035557
 
4.0%
3.031418
 
3.5%
0.080
 
< 0.1%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:38.644455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:38.743323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0718366
91.5%
1.035557
 
4.5%
3.031418
 
4.0%
0.080
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSSEG_OBER
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
2.0
516676 
1.0
151665 
3.0
116737 
0.0
 
343

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0516676
58.0%
1.0151665
 
17.0%
3.0116737
 
13.1%
0.0343
 
< 0.1%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:38.856240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:38.984996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0516676
65.8%
1.0151665
 
19.3%
3.0116737
 
14.9%
0.0343
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSSEG_VAN
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
2.0
487626 
1.0
169327 
3.0
127767 
0.0
 
701

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0487626
54.7%
1.0169327
 
19.0%
3.0127767
 
14.3%
0.0701
 
0.1%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:39.097965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:39.178516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0487626
62.1%
1.0169327
 
21.6%
3.0127767
 
16.3%
0.0701
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSZUL_NEU
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
2.0
378304 
1.0
222157 
3.0
153623 
0.0
 
31337

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0378304
42.4%
1.0222157
24.9%
3.0153623
17.2%
0.031337
 
3.5%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:39.279384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:39.380010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0378304
48.2%
1.0222157
28.3%
3.0153623
19.6%
0.031337
 
4.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KW_0_60
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
357321 
2.0
165557 
4.0
159703 
1.0
54326 
5.0
48514 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0357321
40.1%
2.0165557
18.6%
4.0159703
17.9%
1.054326
 
6.1%
5.048514
 
5.4%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:39.511168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:39.611842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0357321
45.5%
2.0165557
21.1%
4.0159703
20.3%
1.054326
 
6.9%
5.048514
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KW_110
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.404902594
Minimum0
Maximum5
Zeros124216
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:39.722817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.429197979
Coefficient of variation (CV)0.5942851834
Kurtosis-0.6151255069
Mean2.404902594
Median Absolute Deviation (MAD)1
Skewness-0.1867778177
Sum1888861
Variance2.042606863
MonotonicityNot monotonic
2021-12-24T14:05:39.813487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3275679
30.9%
2175418
19.7%
0124216
13.9%
483780
 
9.4%
163943
 
7.2%
562385
 
7.0%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0124216
13.9%
163943
 
7.2%
2175418
19.7%
3275679
30.9%
483780
 
9.4%
562385
 
7.0%
ValueCountFrequency (%)
562385
 
7.0%
483780
 
9.4%
3275679
30.9%
2175418
19.7%
163943
 
7.2%
0124216
13.9%

KBA13_KW_120
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.376123124
Minimum0
Maximum5
Zeros85028
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:39.914225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.505369925
Coefficient of variation (CV)0.6335403707
Kurtosis-1.085804921
Mean2.376123124
Median Absolute Deviation (MAD)1
Skewness0.02742583434
Sum1866257
Variance2.26613861
MonotonicityNot monotonic
2021-12-24T14:05:40.055251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3281415
31.6%
1226709
25.4%
494774
 
10.6%
085028
 
9.5%
573739
 
8.3%
223756
 
2.7%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
085028
 
9.5%
1226709
25.4%
223756
 
2.7%
3281415
31.6%
494774
 
10.6%
573739
 
8.3%
ValueCountFrequency (%)
573739
 
8.3%
494774
 
10.6%
3281415
31.6%
223756
 
2.7%
1226709
25.4%
085028
 
9.5%

KBA13_KW_121
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.506342458
Minimum0
Maximum5
Zeros95195
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:40.186033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.437424207
Coefficient of variation (CV)0.5735146857
Kurtosis-0.6820698445
Mean2.506342458
Median Absolute Deviation (MAD)1
Skewness-0.1469732264
Sum1968534
Variance2.066188352
MonotonicityNot monotonic
2021-12-24T14:05:40.336907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3282845
31.7%
2135033
15.2%
1105706
 
11.9%
095195
 
10.7%
488983
 
10.0%
577659
 
8.7%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
095195
 
10.7%
1105706
 
11.9%
2135033
15.2%
3282845
31.7%
488983
 
10.0%
577659
 
8.7%
ValueCountFrequency (%)
577659
 
8.7%
488983
 
10.0%
3282845
31.7%
2135033
15.2%
1105706
 
11.9%
095195
 
10.7%

KBA13_KW_30
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
1.0
554887 
2.0
142986 
3.0
87548 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0554887
62.3%
2.0142986
 
16.0%
3.087548
 
9.8%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:40.497657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:40.638501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0554887
70.6%
2.0142986
 
18.2%
3.087548
 
11.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KW_40
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.405065564
Minimum0
Maximum5
Zeros99260
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:40.749137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.412106484
Coefficient of variation (CV)0.5871384571
Kurtosis-0.7035299729
Mean2.405065564
Median Absolute Deviation (MAD)1
Skewness-0.1112622062
Sum1888989
Variance1.994044723
MonotonicityNot monotonic
2021-12-24T14:05:40.879815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3283084
31.8%
2135456
15.2%
1121060
13.6%
099260
 
11.1%
485040
 
9.5%
561521
 
6.9%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
099260
 
11.1%
1121060
13.6%
2135456
15.2%
3283084
31.8%
485040
 
9.5%
561521
 
6.9%
ValueCountFrequency (%)
561521
 
6.9%
485040
 
9.5%
3283084
31.8%
2135456
15.2%
1121060
13.6%
099260
 
11.1%

KBA13_KW_50
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.340691171
Minimum0
Maximum5
Zeros143215
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:41.000570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.441927482
Coefficient of variation (CV)0.6160263686
Kurtosis-0.6654361611
Mean2.340691171
Median Absolute Deviation (MAD)1
Skewness-0.1965794704
Sum1838428
Variance2.079154863
MonotonicityNot monotonic
2021-12-24T14:05:41.101136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3273920
30.7%
2181545
20.4%
0143215
16.1%
481007
 
9.1%
555954
 
6.3%
149780
 
5.6%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0143215
16.1%
149780
 
5.6%
2181545
20.4%
3273920
30.7%
481007
 
9.1%
555954
 
6.3%
ValueCountFrequency (%)
555954
 
6.3%
481007
 
9.1%
3273920
30.7%
2181545
20.4%
149780
 
5.6%
0143215
16.1%

KBA13_KW_60
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.291350753
Minimum0
Maximum5
Zeros132144
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:41.211798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.411953977
Coefficient of variation (CV)0.6162103181
Kurtosis-0.6772758285
Mean2.291350753
Median Absolute Deviation (MAD)1
Skewness-0.1356137415
Sum1799675
Variance1.993614032
MonotonicityNot monotonic
2021-12-24T14:05:41.312396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3267832
30.1%
2178524
20.0%
0132144
14.8%
179264
 
8.9%
478418
 
8.8%
549239
 
5.5%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0132144
14.8%
179264
 
8.9%
2178524
20.0%
3267832
30.1%
478418
 
8.8%
549239
 
5.5%
ValueCountFrequency (%)
549239
 
5.5%
478418
 
8.8%
3267832
30.1%
2178524
20.0%
179264
 
8.9%
0132144
14.8%

KBA13_KW_61_120
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
360881 
2.0
164699 
4.0
161381 
5.0
49311 
1.0
49149 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0360881
40.5%
2.0164699
18.5%
4.0161381
18.1%
5.049311
 
5.5%
1.049149
 
5.5%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:41.453223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:41.541755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0360881
45.9%
2.0164699
21.0%
4.0161381
20.5%
5.049311
 
6.3%
1.049149
 
6.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KW_70
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.336340638
Minimum0
Maximum5
Zeros141548
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:41.664014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.43027192
Coefficient of variation (CV)0.612184669
Kurtosis-0.6384107083
Mean2.336340638
Median Absolute Deviation (MAD)1
Skewness-0.2016039605
Sum1835011
Variance2.045677765
MonotonicityNot monotonic
2021-12-24T14:05:41.794834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3276717
31.0%
2184387
20.7%
0141548
15.9%
478915
 
8.9%
554143
 
6.1%
149711
 
5.6%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0141548
15.9%
149711
 
5.6%
2184387
20.7%
3276717
31.0%
478915
 
8.9%
554143
 
6.1%
ValueCountFrequency (%)
554143
 
6.1%
478915
 
8.9%
3276717
31.0%
2184387
20.7%
149711
 
5.6%
0141548
15.9%

KBA13_KW_80
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.310013356
Minimum0
Maximum5
Zeros129268
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:41.913615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.410660844
Coefficient of variation (CV)0.6106721593
Kurtosis-0.6506222787
Mean2.310013356
Median Absolute Deviation (MAD)1
Skewness-0.1381494907
Sum1814333
Variance1.989964017
MonotonicityNot monotonic
2021-12-24T14:05:42.016210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3269221
30.2%
2180524
20.3%
0129268
14.5%
477562
 
8.7%
177214
 
8.7%
551632
 
5.8%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0129268
14.5%
177214
 
8.7%
2180524
20.3%
3269221
30.2%
477562
 
8.7%
551632
 
5.8%
ValueCountFrequency (%)
551632
 
5.8%
477562
 
8.7%
3269221
30.2%
2180524
20.3%
177214
 
8.7%
0129268
14.5%

KBA13_KW_90
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.382883829
Minimum0
Maximum5
Zeros133326
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:42.117057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.43007608
Coefficient of variation (CV)0.6001451108
Kurtosis-0.6102998176
Mean2.382883829
Median Absolute Deviation (MAD)1
Skewness-0.2104813536
Sum1871567
Variance2.045117594
MonotonicityNot monotonic
2021-12-24T14:05:42.217812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3277407
31.1%
2181685
20.4%
0133326
15.0%
482747
 
9.3%
558683
 
6.6%
151573
 
5.8%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0133326
15.0%
151573
 
5.8%
2181685
20.4%
3277407
31.1%
482747
 
9.3%
558683
 
6.6%
ValueCountFrequency (%)
558683
 
6.6%
482747
 
9.3%
3277407
31.1%
2181685
20.4%
151573
 
5.8%
0133326
15.0%

KBA13_MAZDA
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
343060 
4.0
169148 
2.0
156989 
5.0
71832 
1.0
44392 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0343060
38.5%
4.0169148
19.0%
2.0156989
17.6%
5.071832
 
8.1%
1.044392
 
5.0%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:42.358976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:42.467672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0343060
43.7%
4.0169148
21.5%
2.0156989
20.0%
5.071832
 
9.1%
1.044392
 
5.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_MERCEDES
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
340379 
4.0
178995 
2.0
134513 
5.0
82948 
1.0
48586 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0340379
38.2%
4.0178995
20.1%
2.0134513
 
15.1%
5.082948
 
9.3%
1.048586
 
5.5%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:42.598513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:42.691135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0340379
43.3%
4.0178995
22.8%
2.0134513
 
17.1%
5.082948
 
10.6%
1.048586
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_MOTOR
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
474886 
2.0
144655 
4.0
102786 
1.0
63094 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0474886
53.3%
2.0144655
 
16.2%
4.0102786
 
11.5%
1.063094
 
7.1%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:42.811910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:42.902591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0474886
60.5%
2.0144655
 
18.4%
4.0102786
 
13.1%
1.063094
 
8.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_NISSAN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
335457 
4.0
167124 
2.0
160213 
5.0
71427 
1.0
51200 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row5.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0335457
37.6%
4.0167124
18.8%
2.0160213
18.0%
5.071427
 
8.0%
1.051200
 
5.7%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:43.637581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:43.738239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0335457
42.7%
4.0167124
21.3%
2.0160213
20.4%
5.071427
 
9.1%
1.051200
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_OPEL
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
327618 
2.0
164244 
4.0
154681 
1.0
72559 
5.0
66319 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row2.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0327618
36.8%
2.0164244
18.4%
4.0154681
17.4%
1.072559
 
8.1%
5.066319
 
7.4%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:43.869095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:43.969707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0327618
41.7%
2.0164244
20.9%
4.0154681
19.7%
1.072559
 
9.2%
5.066319
 
8.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_PEUGEOT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
340805 
4.0
170378 
2.0
154373 
5.0
70008 
1.0
49857 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0340805
38.2%
4.0170378
19.1%
2.0154373
17.3%
5.070008
 
7.9%
1.049857
 
5.6%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:44.090559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:44.211304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0340805
43.4%
4.0170378
21.7%
2.0154373
19.7%
5.070008
 
8.9%
1.049857
 
6.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_RENAULT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
336384 
4.0
163307 
2.0
160781 
5.0
70384 
1.0
54565 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0336384
37.7%
4.0163307
18.3%
2.0160781
18.0%
5.070384
 
7.9%
1.054565
 
6.1%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:44.352124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:44.463170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0336384
42.8%
4.0163307
20.8%
2.0160781
20.5%
5.070384
 
9.0%
1.054565
 
6.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_GELAENDEWAGEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
345193 
2.0
178645 
4.0
143742 
1.0
67786 
5.0
50055 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row5.0
3rd row3.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.0345193
38.7%
2.0178645
20.0%
4.0143742
16.1%
1.067786
 
7.6%
5.050055
 
5.6%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:44.574044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:44.674876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0345193
44.0%
2.0178645
22.7%
4.0143742
18.3%
1.067786
 
8.6%
5.050055
 
6.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_GROSSRAUMVANS
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
340642 
4.0
169944 
2.0
152717 
5.0
71749 
1.0
50369 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0340642
38.2%
4.0169944
19.1%
2.0152717
17.1%
5.071749
 
8.1%
1.050369
 
5.7%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:44.795745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:44.896320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0340642
43.4%
4.0169944
21.6%
2.0152717
19.4%
5.071749
 
9.1%
1.050369
 
6.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_KLEINST
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
337591 
2.0
161954 
4.0
158570 
1.0
64938 
5.0
62368 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0337591
37.9%
2.0161954
18.2%
4.0158570
17.8%
1.064938
 
7.3%
5.062368
 
7.0%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:45.027296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:45.127887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0337591
43.0%
2.0161954
20.6%
4.0158570
20.2%
1.064938
 
8.3%
5.062368
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_KLEINWAGEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
341514 
2.0
167806 
4.0
152870 
1.0
68565 
5.0
54666 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0341514
38.3%
2.0167806
18.8%
4.0152870
17.2%
1.068565
 
7.7%
5.054666
 
6.1%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:45.259334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:45.370085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0341514
43.5%
2.0167806
21.4%
4.0152870
19.5%
1.068565
 
8.7%
5.054666
 
7.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_KOMPAKTKLASSE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
344398 
2.0
173314 
4.0
142952 
1.0
64602 
5.0
60155 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row4.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0344398
38.6%
2.0173314
19.4%
4.0142952
16.0%
1.064602
 
7.2%
5.060155
 
6.7%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:45.499171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:45.591773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0344398
43.8%
2.0173314
22.1%
4.0142952
18.2%
1.064602
 
8.2%
5.060155
 
7.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_MINIVANS
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
341862 
2.0
161436 
4.0
160044 
5.0
65130 
1.0
56949 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0341862
38.4%
2.0161436
18.1%
4.0160044
18.0%
5.065130
 
7.3%
1.056949
 
6.4%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:45.712949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:45.814045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0341862
43.5%
2.0161436
20.6%
4.0160044
20.4%
5.065130
 
8.3%
1.056949
 
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_MINIWAGEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
339598 
4.0
176093 
2.0
146739 
5.0
77150 
1.0
45841 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0339598
38.1%
4.0176093
19.8%
2.0146739
16.5%
5.077150
 
8.7%
1.045841
 
5.1%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:45.924686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:46.025577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0339598
43.2%
4.0176093
22.4%
2.0146739
18.7%
5.077150
 
9.8%
1.045841
 
5.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_MITTELKLASSE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
337241 
4.0
164192 
2.0
156862 
5.0
73287 
1.0
53839 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0337241
37.8%
4.0164192
18.4%
2.0156862
17.6%
5.073287
 
8.2%
1.053839
 
6.0%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:46.146402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:46.247380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0337241
42.9%
4.0164192
20.9%
2.0156862
20.0%
5.073287
 
9.3%
1.053839
 
6.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_OBEREMITTELKLASSE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
342284 
4.0
184285 
2.0
132852 
5.0
81830 
1.0
44170 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0342284
38.4%
4.0184285
20.7%
2.0132852
 
14.9%
5.081830
 
9.2%
1.044170
 
5.0%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:46.368350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:46.459487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0342284
43.6%
4.0184285
23.5%
2.0132852
 
16.9%
5.081830
 
10.4%
1.044170
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_OBERKLASSE
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.496482778
Minimum0
Maximum5
Zeros86270
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:46.560278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.479815703
Coefficient of variation (CV)0.592760229
Kurtosis-0.8873647102
Mean2.496482778
Median Absolute Deviation (MAD)1
Skewness-0.08086214335
Sum1960790
Variance2.189854516
MonotonicityNot monotonic
2021-12-24T14:05:46.681076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3283488
31.8%
1157682
17.7%
491369
 
10.3%
086270
 
9.7%
584648
 
9.5%
281964
 
9.2%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
086270
 
9.7%
1157682
17.7%
281964
 
9.2%
3283488
31.8%
491369
 
10.3%
584648
 
9.5%
ValueCountFrequency (%)
584648
 
9.5%
491369
 
10.3%
3283488
31.8%
281964
 
9.2%
1157682
17.7%
086270
 
9.7%

KBA13_SEG_SONSTIGE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
352268 
2.0
167674 
4.0
165534 
5.0
64535 
1.0
35410 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row5.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0352268
39.5%
2.0167674
18.8%
4.0165534
18.6%
5.064535
 
7.2%
1.035410
 
4.0%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:46.821954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:46.912693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0352268
44.9%
2.0167674
21.3%
4.0165534
21.1%
5.064535
 
8.2%
1.035410
 
4.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_SPORTWAGEN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.566941806
Minimum0
Maximum5
Zeros83421
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:47.003437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.437284235
Coefficient of variation (CV)0.5599208487
Kurtosis-0.6774174457
Mean2.566941806
Median Absolute Deviation (MAD)1
Skewness-0.1182886889
Sum2016130
Variance2.065785971
MonotonicityNot monotonic
2021-12-24T14:05:47.104602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3267922
30.1%
2146930
16.5%
1106267
 
11.9%
492168
 
10.3%
588713
 
10.0%
083421
 
9.4%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
083421
 
9.4%
1106267
 
11.9%
2146930
16.5%
3267922
30.1%
492168
 
10.3%
588713
 
10.0%
ValueCountFrequency (%)
588713
 
10.0%
492168
 
10.3%
3267922
30.1%
2146930
16.5%
1106267
 
11.9%
083421
 
9.4%

KBA13_SEG_UTILITIES
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
346456 
2.0
164012 
4.0
160080 
5.0
61579 
1.0
53294 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row2.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0346456
38.9%
2.0164012
18.4%
4.0160080
18.0%
5.061579
 
6.9%
1.053294
 
6.0%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:47.235690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:47.336430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0346456
44.1%
2.0164012
20.9%
4.0160080
20.4%
5.061579
 
7.8%
1.053294
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_VAN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
341438 
4.0
165710 
2.0
157763 
5.0
67515 
1.0
52995 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0341438
38.3%
4.0165710
18.6%
2.0157763
17.7%
5.067515
 
7.6%
1.052995
 
5.9%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:47.457226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:47.548159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0341438
43.5%
4.0165710
21.1%
2.0157763
20.1%
5.067515
 
8.6%
1.052995
 
6.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_WOHNMOBILE
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.538732731
Minimum0
Maximum5
Zeros85796
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:47.646857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4113662
Coefficient of variation (CV)0.5559333531
Kurtosis-0.5925733501
Mean2.538732731
Median Absolute Deviation (MAD)1
Skewness-0.1201331485
Sum1993974
Variance1.991954549
MonotonicityNot monotonic
2021-12-24T14:05:47.747595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3269140
30.2%
2165205
18.5%
195326
 
10.7%
488952
 
10.0%
085796
 
9.6%
581002
 
9.1%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
085796
 
9.6%
195326
 
10.7%
2165205
18.5%
3269140
30.2%
488952
 
10.0%
581002
 
9.1%
ValueCountFrequency (%)
581002
 
9.1%
488952
 
10.0%
3269140
30.2%
2165205
18.5%
195326
 
10.7%
085796
 
9.6%

KBA13_SITZE_4
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
328454 
4.0
181695 
2.0
129303 
5.0
93443 
1.0
52526 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0328454
36.9%
4.0181695
20.4%
2.0129303
 
14.5%
5.093443
 
10.5%
1.052526
 
5.9%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:47.870364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:47.970946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0328454
41.8%
4.0181695
23.1%
2.0129303
 
16.5%
5.093443
 
11.9%
1.052526
 
6.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SITZE_5
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
330228 
2.0
179693 
4.0
128787 
1.0
91495 
5.0
55218 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0330228
37.1%
2.0179693
20.2%
4.0128787
 
14.5%
1.091495
 
10.3%
5.055218
 
6.2%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:48.081722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:48.180749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0330228
42.0%
2.0179693
22.9%
4.0128787
 
16.4%
1.091495
 
11.6%
5.055218
 
7.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SITZE_6
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
336224 
4.0
166528 
2.0
147292 
5.0
76974 
1.0
58403 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0336224
37.7%
4.0166528
18.7%
2.0147292
16.5%
5.076974
 
8.6%
1.058403
 
6.6%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:48.301722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:48.394412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0336224
42.8%
4.0166528
21.2%
2.0147292
18.8%
5.076974
 
9.8%
1.058403
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_TOYOTA
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
343193 
4.0
166426 
2.0
156050 
5.0
72002 
1.0
47750 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0343193
38.5%
4.0166426
18.7%
2.0156050
17.5%
5.072002
 
8.1%
1.047750
 
5.4%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:48.543495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:48.636277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0343193
43.7%
4.0166426
21.2%
2.0156050
19.9%
5.072002
 
9.2%
1.047750
 
6.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_0
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
349837 
4.0
174276 
2.0
153752 
5.0
71730 
1.0
35826 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0349837
39.3%
4.0174276
19.6%
2.0153752
17.3%
5.071730
 
8.0%
1.035826
 
4.0%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:48.757320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:48.858177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0349837
44.5%
4.0174276
22.2%
2.0153752
19.6%
5.071730
 
9.1%
1.035826
 
4.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
361449 
2.0
167076 
4.0
158150 
1.0
50939 
5.0
47807 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0361449
40.6%
2.0167076
18.7%
4.0158150
17.7%
1.050939
 
5.7%
5.047807
 
5.4%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:49.009082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:49.117743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0361449
46.0%
2.0167076
21.3%
4.0158150
20.1%
1.050939
 
6.5%
5.047807
 
6.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_1_2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
359262 
2.0
173047 
4.0
151120 
1.0
61834 
5.0
40158 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0359262
40.3%
2.0173047
19.4%
4.0151120
17.0%
1.061834
 
6.9%
5.040158
 
4.5%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:49.250500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:49.349291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0359262
45.7%
2.0173047
22.0%
4.0151120
19.2%
1.061834
 
7.9%
5.040158
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
363866 
2.0
166317 
4.0
162515 
5.0
49491 
1.0
43232 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0363866
40.8%
2.0166317
18.7%
4.0162515
18.2%
5.049491
 
5.6%
1.043232
 
4.9%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:49.463070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:49.580789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0363866
46.3%
2.0166317
21.2%
4.0162515
20.7%
5.049491
 
6.3%
1.043232
 
5.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_3
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean2.354173112
Minimum0
Maximum5
Zeros143284
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:49.693420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.469417336
Coefficient of variation (CV)0.6241755664
Kurtosis-0.7135883338
Mean2.354173112
Median Absolute Deviation (MAD)1
Skewness-0.1573543125
Sum1849017
Variance2.159187307
MonotonicityNot monotonic
2021-12-24T14:05:49.802074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3264685
29.7%
2177458
19.9%
0143284
16.1%
481176
 
9.1%
564131
 
7.2%
154687
 
6.1%
(Missing)105800
 
11.9%
ValueCountFrequency (%)
0143284
16.1%
154687
 
6.1%
2177458
19.9%
3264685
29.7%
481176
 
9.1%
564131
 
7.2%
ValueCountFrequency (%)
564131
 
7.2%
481176
 
9.1%
3264685
29.7%
2177458
19.9%
154687
 
6.1%
0143284
16.1%

KBA13_VW
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing105800
Missing (%)11.9%
Memory size6.8 MiB
3.0
336588 
2.0
171175 
4.0
149018 
1.0
71506 
5.0
57134 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row4.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0336588
37.8%
2.0171175
19.2%
4.0149018
16.7%
1.071506
 
8.0%
5.057134
 
6.4%
(Missing)105800
 
11.9%

Length

2021-12-24T14:05:49.924877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:50.016015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0336588
42.9%
2.0171175
21.8%
4.0149018
19.0%
1.071506
 
9.1%
5.057134
 
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KK_KUNDENTYP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing584612
Missing (%)65.6%
Infinite0
Infinite (%)0.0%
Mean3.410640262
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:50.106668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.628844193
Coefficient of variation (CV)0.4775772489
Kurtosis-1.160216118
Mean3.410640262
Median Absolute Deviation (MAD)1
Skewness0.1480185173
Sum1045733
Variance2.653133406
MonotonicityNot monotonic
2021-12-24T14:05:50.227691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
365151
 
7.3%
262564
 
7.0%
548038
 
5.4%
444512
 
5.0%
644114
 
4.9%
142230
 
4.7%
(Missing)584612
65.6%
ValueCountFrequency (%)
142230
4.7%
262564
7.0%
365151
7.3%
444512
5.0%
548038
5.4%
644114
4.9%
ValueCountFrequency (%)
644114
4.9%
548038
5.4%
444512
5.0%
365151
7.3%
262564
7.0%
142230
4.7%

KKK
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing121196
Missing (%)13.6%
Memory size6.8 MiB
3.0
273024 
2.0
181519 
4.0
178648 
1.0
99966 
0.0
36868 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row0.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0273024
30.6%
2.0181519
20.4%
4.0178648
20.0%
1.099966
 
11.2%
0.036868
 
4.1%
(Missing)121196
13.6%

Length

2021-12-24T14:05:50.368999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:50.459755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0273024
35.5%
2.0181519
23.6%
4.0178648
23.2%
1.099966
 
13.0%
0.036868
 
4.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KOMBIALTER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
4
272770 
3
246214 
2
183764 
1
94779 
9
93694 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row1
3rd row2
4th row4
5th row3

Common Values

ValueCountFrequency (%)
4272770
30.6%
3246214
27.6%
2183764
20.6%
194779
 
10.6%
993694
 
10.5%

Length

2021-12-24T14:05:50.570688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:50.661299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4272770
30.6%
3246214
27.6%
2183764
20.6%
194779
 
10.6%
993694
 
10.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KONSUMNAEHE
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing73969
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean3.018452081
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:50.772231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.550311878
Coefficient of variation (CV)0.5136115586
Kurtosis-1.089230428
Mean3.018452081
Median Absolute Deviation (MAD)1
Skewness0.1833736883
Sum2466836
Variance2.403466919
MonotonicityNot monotonic
2021-12-24T14:05:50.880935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1193738
21.7%
3171127
19.2%
5153535
17.2%
2134665
15.1%
4133324
15.0%
626625
 
3.0%
74238
 
0.5%
(Missing)73969
 
8.3%
ValueCountFrequency (%)
1193738
21.7%
2134665
15.1%
3171127
19.2%
4133324
15.0%
5153535
17.2%
626625
 
3.0%
74238
 
0.5%
ValueCountFrequency (%)
74238
 
0.5%
626625
 
3.0%
5153535
17.2%
4133324
15.0%
3171127
19.2%
2134665
15.1%
1193738
21.7%

KONSUMZELLE
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing93155
Missing (%)10.5%
Memory size6.8 MiB
0.0
609591 
1.0
188475 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0609591
68.4%
1.0188475
 
21.1%
(Missing)93155
 
10.5%

Length

2021-12-24T14:05:51.034108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:51.122709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0609591
76.4%
1.0188475
 
23.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LP_FAMILIE_FEIN
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean3.599574443
Minimum0
Maximum11
Zeros72938
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:51.215453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.926486236
Coefficient of variation (CV)1.090819567
Kurtosis-0.9106001239
Mean3.599574443
Median Absolute Deviation (MAD)1
Skewness0.9410430432
Sum3190544
Variance15.41729416
MonotonicityNot monotonic
2021-12-24T14:05:51.356429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1426379
47.8%
10137913
 
15.5%
2104305
 
11.7%
072938
 
8.2%
1151719
 
5.8%
823032
 
2.6%
720730
 
2.3%
412303
 
1.4%
511920
 
1.3%
911148
 
1.3%
Other values (2)13980
 
1.6%
ValueCountFrequency (%)
072938
 
8.2%
1426379
47.8%
2104305
 
11.7%
34958
 
0.6%
412303
 
1.4%
511920
 
1.3%
69022
 
1.0%
720730
 
2.3%
823032
 
2.6%
911148
 
1.3%
ValueCountFrequency (%)
1151719
 
5.8%
10137913
15.5%
911148
 
1.3%
823032
 
2.6%
720730
 
2.3%
69022
 
1.0%
511920
 
1.3%
412303
 
1.4%
34958
 
0.6%
2104305
11.7%

LP_FAMILIE_GROB
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.185965858
Minimum0
Maximum5
Zeros72938
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:51.495100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.756537367
Coefficient of variation (CV)0.803552059
Kurtosis-1.141298857
Mean2.185965858
Median Absolute Deviation (MAD)1
Skewness0.6934485098
Sum1937568
Variance3.08542352
MonotonicityNot monotonic
2021-12-24T14:05:51.617802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1426379
47.8%
5200780
22.5%
2104305
 
11.7%
072938
 
8.2%
452784
 
5.9%
329181
 
3.3%
(Missing)4854
 
0.5%
ValueCountFrequency (%)
072938
 
8.2%
1426379
47.8%
2104305
 
11.7%
329181
 
3.3%
452784
 
5.9%
5200780
22.5%
ValueCountFrequency (%)
5200780
22.5%
452784
 
5.9%
329181
 
3.3%
2104305
 
11.7%
1426379
47.8%
072938
 
8.2%

LP_LEBENSPHASE_FEIN
Real number (ℝ≥0)

ZEROS

Distinct41
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean14.62263712
Minimum0
Maximum40
Zeros92778
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:51.789047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q327
95-th percentile38
Maximum40
Range40
Interquartile range (IQR)23

Descriptive statistics

Standard deviation12.61688274
Coefficient of variation (CV)0.8628322399
Kurtosis-1.060101299
Mean14.62263712
Median Absolute Deviation (MAD)9
Skewness0.5547653618
Sum12961023
Variance159.1857302
MonotonicityNot monotonic
2021-12-24T14:05:51.970572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
092778
 
10.4%
162667
 
7.0%
555542
 
6.2%
645614
 
5.1%
239434
 
4.4%
830475
 
3.4%
1126710
 
3.0%
2926577
 
3.0%
726508
 
3.0%
1326085
 
2.9%
Other values (31)453977
50.9%
ValueCountFrequency (%)
092778
10.4%
162667
7.0%
239434
4.4%
319985
 
2.2%
417595
 
2.0%
555542
6.2%
645614
5.1%
726508
 
3.0%
830475
 
3.4%
913066
 
1.5%
ValueCountFrequency (%)
4015150
1.7%
3916182
1.8%
3813914
1.6%
3718525
2.1%
3610505
1.2%
3513679
1.5%
3413074
1.5%
336066
 
0.7%
3217105
1.9%
3123987
2.7%

LP_LEBENSPHASE_GROB
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean4.453621355
Minimum0
Maximum12
Zeros89718
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:52.091374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.855639394
Coefficient of variation (CV)0.8657312974
Kurtosis-0.8136004432
Mean4.453621355
Median Absolute Deviation (MAD)2
Skewness0.7519252624
Sum3947543
Variance14.86595514
MonotonicityNot monotonic
2021-12-24T14:05:52.212109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2158139
17.7%
1139681
15.7%
3115624
13.0%
089718
10.1%
1274276
8.3%
454443
 
6.1%
549672
 
5.6%
948938
 
5.5%
1041092
 
4.6%
1132819
 
3.7%
Other values (3)81965
9.2%
ValueCountFrequency (%)
089718
10.1%
1139681
15.7%
2158139
17.7%
3115624
13.0%
454443
 
6.1%
549672
 
5.6%
629181
 
3.3%
722461
 
2.5%
830323
 
3.4%
948938
 
5.5%
ValueCountFrequency (%)
1274276
8.3%
1132819
 
3.7%
1041092
 
4.6%
948938
5.5%
830323
 
3.4%
722461
 
2.5%
629181
 
3.3%
549672
5.6%
454443
6.1%
3115624
13.0%

LP_STATUS_FEIN
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean4.791150844
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:52.332924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.425305064
Coefficient of variation (CV)0.7149232357
Kurtosis-1.463356931
Mean4.791150844
Median Absolute Deviation (MAD)3
Skewness0.3702895041
Sum4246718
Variance11.73271478
MonotonicityNot monotonic
2021-12-24T14:05:52.433673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1219275
24.6%
9143238
16.1%
2118236
13.3%
10118022
13.2%
478317
 
8.8%
574493
 
8.4%
374105
 
8.3%
630914
 
3.5%
819708
 
2.2%
710059
 
1.1%
(Missing)4854
 
0.5%
ValueCountFrequency (%)
1219275
24.6%
2118236
13.3%
374105
 
8.3%
478317
 
8.8%
574493
 
8.4%
630914
 
3.5%
710059
 
1.1%
819708
 
2.2%
9143238
16.1%
10118022
13.2%
ValueCountFrequency (%)
10118022
13.2%
9143238
16.1%
819708
 
2.2%
710059
 
1.1%
630914
 
3.5%
574493
 
8.4%
478317
 
8.8%
374105
 
8.3%
2118236
13.3%
1219275
24.6%

LP_STATUS_GROB
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Memory size6.8 MiB
1.0
337511 
2.0
226915 
4.0
162946 
5.0
118022 
3.0
40973 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0337511
37.9%
2.0226915
25.5%
4.0162946
18.3%
5.0118022
 
13.2%
3.040973
 
4.6%
(Missing)4854
 
0.5%

Length

2021-12-24T14:05:52.554626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:52.653306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0337511
38.1%
2.0226915
25.6%
4.0162946
18.4%
5.0118022
 
13.3%
3.040973
 
4.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MIN_GEBAEUDEJAHR
Real number (ℝ≥0)

MISSING

Distinct32
Distinct (%)< 0.1%
Missing93148
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean1993.277011
Minimum1985
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:52.796721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1985
5-th percentile1992
Q11992
median1992
Q31993
95-th percentile2000
Maximum2016
Range31
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.332738997
Coefficient of variation (CV)0.001671989883
Kurtosis14.22772176
Mean1993.277011
Median Absolute Deviation (MAD)0
Skewness3.556034362
Sum1590780564
Variance11.10714922
MonotonicityNot monotonic
2021-12-24T14:05:52.917893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1992568776
63.8%
199478835
 
8.8%
199325488
 
2.9%
199525464
 
2.9%
199616611
 
1.9%
199714464
 
1.6%
20007382
 
0.8%
20015877
 
0.7%
19915811
 
0.7%
20055553
 
0.6%
Other values (22)43812
 
4.9%
(Missing)93148
 
10.5%
ValueCountFrequency (%)
1985116
 
< 0.1%
1986125
 
< 0.1%
1987470
 
0.1%
19881027
 
0.1%
19892046
 
0.2%
19904408
 
0.5%
19915811
 
0.7%
1992568776
63.8%
199325488
 
2.9%
199478835
 
8.8%
ValueCountFrequency (%)
2016128
 
< 0.1%
2015717
 
0.1%
20141001
0.1%
20131230
0.1%
20121861
0.2%
20111903
0.2%
20101410
0.2%
20092016
0.2%
20082197
0.2%
20072156
0.2%

MOBI_RASTER
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing93148
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean2.37881873
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:53.048862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.536927473
Coefficient of variation (CV)0.6460885202
Kurtosis-0.6950184903
Mean2.37881873
Median Absolute Deviation (MAD)1
Skewness0.7552069291
Sum1898471
Variance2.362146056
MonotonicityNot monotonic
2021-12-24T14:05:53.159705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1355579
39.9%
3124055
 
13.9%
2118231
 
13.3%
492804
 
10.4%
581375
 
9.1%
626029
 
2.9%
(Missing)93148
 
10.5%
ValueCountFrequency (%)
1355579
39.9%
2118231
 
13.3%
3124055
 
13.9%
492804
 
10.4%
581375
 
9.1%
626029
 
2.9%
ValueCountFrequency (%)
626029
 
2.9%
581375
 
9.1%
492804
 
10.4%
3124055
 
13.9%
2118231
 
13.3%
1355579
39.9%

MOBI_REGIO
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing133324
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean2.963539901
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:53.288589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.428882174
Coefficient of variation (CV)0.4821538506
Kurtosis-1.315601408
Mean2.963539901
Median Absolute Deviation (MAD)1
Skewness0.02630554031
Sum2246058
Variance2.041704268
MonotonicityNot monotonic
2021-12-24T14:05:53.391165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1163993
18.4%
3150336
16.9%
5148713
16.7%
4148209
16.6%
2146305
16.4%
6341
 
< 0.1%
(Missing)133324
15.0%
ValueCountFrequency (%)
1163993
18.4%
2146305
16.4%
3150336
16.9%
4148209
16.6%
5148713
16.7%
6341
 
< 0.1%
ValueCountFrequency (%)
6341
 
< 0.1%
5148713
16.7%
4148209
16.6%
3150336
16.9%
2146305
16.4%
1163993
18.4%

NATIONALITAET_KZ
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
1
684085 
0
108315 
2
 
65418
3
 
33403

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1684085
76.8%
0108315
 
12.2%
265418
 
7.3%
333403
 
3.7%

Length

2021-12-24T14:05:53.532584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:53.613043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1684085
76.8%
0108315
 
12.2%
265418
 
7.3%
333403
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ONLINE_AFFINITAET
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.6986914
Minimum0
Maximum5
Zeros65716
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:53.713951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.52152361
Coefficient of variation (CV)0.5638005184
Kurtosis-1.066834259
Mean2.6986914
Median Absolute Deviation (MAD)1
Skewness-0.02418530245
Sum2392031
Variance2.315034097
MonotonicityNot monotonic
2021-12-24T14:05:53.824607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2197850
22.2%
4164704
18.5%
3163487
18.3%
1156499
17.6%
5138111
15.5%
065716
 
7.4%
(Missing)4854
 
0.5%
ValueCountFrequency (%)
065716
 
7.4%
1156499
17.6%
2197850
22.2%
3163487
18.3%
4164704
18.5%
5138111
15.5%
ValueCountFrequency (%)
5138111
15.5%
4164704
18.5%
3163487
18.3%
2197850
22.2%
1156499
17.6%
065716
 
7.4%

ORTSGR_KLS9
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)< 0.1%
Missing97216
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean5.293001933
Minimum0
Maximum9
Zeros58
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:53.955375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.303738703
Coefficient of variation (CV)0.4352423694
Kurtosis-0.9404036909
Mean5.293001933
Median Absolute Deviation (MAD)2
Skewness-0.008312889998
Sum4202670
Variance5.307212011
MonotonicityNot monotonic
2021-12-24T14:05:54.046121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5148096
16.6%
4114909
12.9%
7102866
11.5%
991879
10.3%
383542
9.4%
675995
8.5%
872709
8.2%
263362
7.1%
140589
 
4.6%
058
 
< 0.1%
(Missing)97216
10.9%
ValueCountFrequency (%)
058
 
< 0.1%
140589
 
4.6%
263362
7.1%
383542
9.4%
4114909
12.9%
5148096
16.6%
675995
8.5%
7102866
11.5%
872709
8.2%
991879
10.3%
ValueCountFrequency (%)
991879
10.3%
872709
8.2%
7102866
11.5%
675995
8.5%
5148096
16.6%
4114909
12.9%
383542
9.4%
263362
7.1%
140589
 
4.6%
058
 
< 0.1%

OST_WEST_KZ
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing93148
Missing (%)10.5%
Memory size6.8 MiB
W
629528 
O
168545 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowW
3rd rowW
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
W629528
70.6%
O168545
 
18.9%
(Missing)93148
 
10.5%

Length

2021-12-24T14:05:54.185385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:54.296049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
w629528
78.9%
o168545
 
21.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_ANTG1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing116515
Missing (%)13.1%
Memory size6.8 MiB
2.0
270590 
3.0
222355 
1.0
189247 
4.0
87044 
0.0
 
5470

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0270590
30.4%
3.0222355
24.9%
1.0189247
21.2%
4.087044
 
9.8%
0.05470
 
0.6%
(Missing)116515
13.1%

Length

2021-12-24T14:05:54.378509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:54.469002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0270590
34.9%
3.0222355
28.7%
1.0189247
24.4%
4.087044
 
11.2%
0.05470
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_ANTG2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing116515
Missing (%)13.1%
Memory size6.8 MiB
3.0
307283 
2.0
215767 
4.0
191005 
1.0
53213 
0.0
 
7438

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0307283
34.5%
2.0215767
24.2%
4.0191005
21.4%
1.053213
 
6.0%
0.07438
 
0.8%
(Missing)116515
 
13.1%

Length

2021-12-24T14:05:54.610072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:54.700916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0307283
39.7%
2.0215767
27.9%
4.0191005
24.7%
1.053213
 
6.9%
0.07438
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_ANTG3
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing116515
Missing (%)13.1%
Memory size6.8 MiB
2.0
252994 
1.0
237878 
3.0
164040 
0.0
119794 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0252994
28.4%
1.0237878
26.7%
3.0164040
18.4%
0.0119794
13.4%
(Missing)116515
13.1%

Length

2021-12-24T14:05:54.842072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:54.952709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0252994
32.7%
1.0237878
30.7%
3.0164040
21.2%
0.0119794
15.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_ANTG4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing116515
Missing (%)13.1%
Memory size6.8 MiB
0.0
356389 
1.0
294986 
2.0
123331 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0356389
40.0%
1.0294986
33.1%
2.0123331
 
13.8%
(Missing)116515
 
13.1%

Length

2021-12-24T14:05:55.073536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:55.162075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0356389
46.0%
1.0294986
38.1%
2.0123331
 
15.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_BAUMAX
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing116515
Missing (%)13.1%
Memory size6.8 MiB
1.0
499550 
5.0
97333 
2.0
70407 
4.0
56684 
3.0
50732 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0499550
56.1%
5.097333
 
10.9%
2.070407
 
7.9%
4.056684
 
6.4%
3.050732
 
5.7%
(Missing)116515
 
13.1%

Length

2021-12-24T14:05:55.244663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:55.335022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0499550
64.5%
5.097333
 
12.6%
2.070407
 
9.1%
4.056684
 
7.3%
3.050732
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_GBZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing116515
Missing (%)13.1%
Memory size6.8 MiB
3.0
288383 
4.0
180252 
5.0
153883 
2.0
111588 
1.0
40600 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.0288383
32.4%
4.0180252
20.2%
5.0153883
17.3%
2.0111588
 
12.5%
1.040600
 
4.6%
(Missing)116515
13.1%

Length

2021-12-24T14:05:55.455860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:55.556565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0288383
37.2%
4.0180252
23.3%
5.0153883
19.9%
2.0111588
 
14.4%
1.040600
 
5.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_HHZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing116515
Missing (%)13.1%
Memory size6.8 MiB
3.0
309146 
4.0
211911 
5.0
175813 
2.0
66891 
1.0
 
10945

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row3.0
4th row3.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.0309146
34.7%
4.0211911
23.8%
5.0175813
19.7%
2.066891
 
7.5%
1.010945
 
1.2%
(Missing)116515
 
13.1%

Length

2021-12-24T14:05:55.657260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:55.748938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0309146
39.9%
4.0211911
27.4%
5.0175813
22.7%
2.066891
 
8.6%
1.010945
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PRAEGENDE_JUGENDJAHRE
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.154345555
Minimum0
Maximum15
Zeros108164
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:55.848637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median8
Q314
95-th percentile14
Maximum15
Range15
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.844532197
Coefficient of variation (CV)0.5941043538
Kurtosis-1.11662633
Mean8.154345555
Median Absolute Deviation (MAD)4
Skewness-0.2507805175
Sum7267324
Variance23.4694922
MonotonicityNot monotonic
2021-12-24T14:05:55.959538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
14188697
21.2%
8145988
16.4%
0108164
12.1%
586416
9.7%
1085808
9.6%
355195
 
6.2%
1542547
 
4.8%
1135752
 
4.0%
933570
 
3.8%
625652
 
2.9%
Other values (6)83432
9.4%
ValueCountFrequency (%)
0108164
12.1%
121282
 
2.4%
27479
 
0.8%
355195
 
6.2%
420451
 
2.3%
586416
9.7%
625652
 
2.9%
74010
 
0.4%
8145988
16.4%
933570
 
3.8%
ValueCountFrequency (%)
1542547
 
4.8%
14188697
21.2%
135764
 
0.6%
1224446
 
2.7%
1135752
 
4.0%
1085808
9.6%
933570
 
3.8%
8145988
16.4%
74010
 
0.4%
625652
 
2.9%

REGIOTYP
Real number (ℝ≥0)

MISSING
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing121196
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean4.257966949
Minimum0
Maximum7
Zeros36868
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:56.100575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.030384535
Coefficient of variation (CV)0.4768436579
Kurtosis-0.8986656455
Mean4.257966949
Median Absolute Deviation (MAD)1
Skewness-0.4906113801
Sum3278741
Variance4.122461361
MonotonicityNot monotonic
2021-12-24T14:05:56.221501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
6195286
21.9%
5145359
16.3%
393929
10.5%
291662
10.3%
783943
9.4%
468180
 
7.7%
154798
 
6.1%
036868
 
4.1%
(Missing)121196
13.6%
ValueCountFrequency (%)
036868
 
4.1%
154798
 
6.1%
291662
10.3%
393929
10.5%
468180
 
7.7%
5145359
16.3%
6195286
21.9%
783943
9.4%
ValueCountFrequency (%)
783943
9.4%
6195286
21.9%
5145359
16.3%
468180
 
7.7%
393929
10.5%
291662
10.3%
154798
 
6.1%
036868
 
4.1%

RELAT_AB
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing97216
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean3.072219948
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:56.350514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.362980445
Coefficient of variation (CV)0.4436467662
Kurtosis-1.00801365
Mean3.072219948
Median Absolute Deviation (MAD)1
Skewness-0.01830679369
Sum2439358
Variance1.857715694
MonotonicityNot monotonic
2021-12-24T14:05:56.443372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3274008
30.7%
5174964
19.6%
1142907
16.0%
2104846
 
11.8%
497121
 
10.9%
9159
 
< 0.1%
(Missing)97216
 
10.9%
ValueCountFrequency (%)
1142907
16.0%
2104846
 
11.8%
3274008
30.7%
497121
 
10.9%
5174964
19.6%
9159
 
< 0.1%
ValueCountFrequency (%)
9159
 
< 0.1%
5174964
19.6%
497121
 
10.9%
3274008
30.7%
2104846
 
11.8%
1142907
16.0%

RETOURTYP_BK_S
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Memory size6.8 MiB
5.0
297993 
3.0
231816 
4.0
131115 
1.0
129712 
2.0
95731 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row3.0
4th row2.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0297993
33.4%
3.0231816
26.0%
4.0131115
14.7%
1.0129712
14.6%
2.095731
 
10.7%
(Missing)4854
 
0.5%

Length

2021-12-24T14:05:56.594642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:56.685317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0297993
33.6%
3.0231816
26.2%
4.0131115
14.8%
1.0129712
14.6%
2.095731
 
10.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RT_KEIN_ANREIZ
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Memory size6.8 MiB
5.0
211534 
4.0
206707 
3.0
186655 
1.0
141140 
2.0
140331 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row5.0
3rd row5.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0211534
23.7%
4.0206707
23.2%
3.0186655
20.9%
1.0141140
15.8%
2.0140331
15.7%
(Missing)4854
 
0.5%

Length

2021-12-24T14:05:56.806119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:56.906789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0211534
23.9%
4.0206707
23.3%
3.0186655
21.1%
1.0141140
15.9%
2.0140331
15.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RT_SCHNAEPPCHEN
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4854
Missing (%)0.5%
Memory size6.8 MiB
5.0
402504 
4.0
182059 
3.0
133538 
2.0
115106 
1.0
53160 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row2.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0402504
45.2%
4.0182059
20.4%
3.0133538
 
15.0%
2.0115106
 
12.9%
1.053160
 
6.0%
(Missing)4854
 
0.5%

Length

2021-12-24T14:05:57.027724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:05:57.118715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0402504
45.4%
4.0182059
20.5%
3.0133538
 
15.1%
2.0115106
 
13.0%
1.053160
 
6.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RT_UEBERGROESSE
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing51226
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean3.023813237
Minimum0
Maximum5
Zeros24758
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:57.219552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.502725386
Coefficient of variation (CV)0.4969636907
Kurtosis-1.18388437
Mean3.023813237
Median Absolute Deviation (MAD)1
Skewness-0.1665382214
Sum2539988
Variance2.258183587
MonotonicityNot monotonic
2021-12-24T14:05:57.330446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5188774
21.2%
4168467
18.9%
3155865
17.5%
2152524
17.1%
1149607
16.8%
024758
 
2.8%
(Missing)51226
 
5.7%
ValueCountFrequency (%)
024758
 
2.8%
1149607
16.8%
2152524
17.1%
3155865
17.5%
4168467
18.9%
5188774
21.2%
ValueCountFrequency (%)
5188774
21.2%
4168467
18.9%
3155865
17.5%
2152524
17.1%
1149607
16.8%
024758
 
2.8%

SEMIO_DOM
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.667550473
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:57.441281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.79571208
Coefficient of variation (CV)0.3847225843
Kurtosis-0.9093931002
Mean4.667550473
Median Absolute Deviation (MAD)1
Skewness-0.4131770759
Sum4159819
Variance3.224581876
MonotonicityNot monotonic
2021-12-24T14:05:57.542003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6183435
20.6%
5177889
20.0%
7161495
18.1%
4125115
14.0%
2101498
11.4%
397027
10.9%
144762
 
5.0%
ValueCountFrequency (%)
144762
 
5.0%
2101498
11.4%
397027
10.9%
4125115
14.0%
5177889
20.0%
6183435
20.6%
7161495
18.1%
ValueCountFrequency (%)
7161495
18.1%
6183435
20.6%
5177889
20.0%
4125115
14.0%
397027
10.9%
2101498
11.4%
144762
 
5.0%

SEMIO_ERL
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.481404725
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:57.653057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.807551899
Coefficient of variation (CV)0.4033449354
Kurtosis-1.105118991
Mean4.481404725
Median Absolute Deviation (MAD)1
Skewness-0.04328868758
Sum3993922
Variance3.267243868
MonotonicityNot monotonic
2021-12-24T14:05:57.764460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4196206
22.0%
3180824
20.3%
7179141
20.1%
6139209
15.6%
277012
 
8.6%
576133
 
8.5%
142696
 
4.8%
ValueCountFrequency (%)
142696
 
4.8%
277012
 
8.6%
3180824
20.3%
4196206
22.0%
576133
 
8.5%
6139209
15.6%
7179141
20.1%
ValueCountFrequency (%)
7179141
20.1%
6139209
15.6%
576133
 
8.5%
4196206
22.0%
3180824
20.3%
277012
 
8.6%
142696
 
4.8%

SEMIO_FAM
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.272729211
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:57.895519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.915885028
Coefficient of variation (CV)0.4483984202
Kurtosis-1.19892824
Mean4.272729211
Median Absolute Deviation (MAD)2
Skewness-0.2058485462
Sum3807946
Variance3.67061544
MonotonicityNot monotonic
2021-12-24T14:05:58.006317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6186729
21.0%
2139562
15.7%
4135942
15.3%
5133740
15.0%
7118517
13.3%
394815
10.6%
181916
9.2%
ValueCountFrequency (%)
181916
9.2%
2139562
15.7%
394815
10.6%
4135942
15.3%
5133740
15.0%
6186729
21.0%
7118517
13.3%
ValueCountFrequency (%)
7118517
13.3%
6186729
21.0%
5133740
15.0%
4135942
15.3%
394815
10.6%
2139562
15.7%
181916
9.2%

SEMIO_KAEM
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.445007467
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:58.127278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.852412242
Coefficient of variation (CV)0.4167399618
Kurtosis-1.236079929
Mean4.445007467
Median Absolute Deviation (MAD)2
Skewness-0.1927373604
Sum3961484
Variance3.431431115
MonotonicityNot monotonic
2021-12-24T14:05:58.228185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6206001
23.1%
3180955
20.3%
7135579
15.2%
5128501
14.4%
2114038
12.8%
478944
 
8.9%
147203
 
5.3%
ValueCountFrequency (%)
147203
 
5.3%
2114038
12.8%
3180955
20.3%
478944
 
8.9%
5128501
14.4%
6206001
23.1%
7135579
15.2%
ValueCountFrequency (%)
7135579
15.2%
6206001
23.1%
5128501
14.4%
478944
 
8.9%
3180955
20.3%
2114038
12.8%
147203
 
5.3%

SEMIO_KRIT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.76322259
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:58.339530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.830789358
Coefficient of variation (CV)0.3843593961
Kurtosis-0.8752505716
Mean4.76322259
Median Absolute Deviation (MAD)2
Skewness-0.3882238006
Sum4245084
Variance3.351789675
MonotonicityNot monotonic
2021-12-24T14:05:58.430433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7219847
24.7%
5156298
17.5%
4144079
16.2%
6133049
14.9%
3129106
14.5%
154947
 
6.2%
253895
 
6.0%
ValueCountFrequency (%)
154947
 
6.2%
253895
 
6.0%
3129106
14.5%
4144079
16.2%
5156298
17.5%
6133049
14.9%
7219847
24.7%
ValueCountFrequency (%)
7219847
24.7%
6133049
14.9%
5156298
17.5%
4144079
16.2%
3129106
14.5%
253895
 
6.0%
154947
 
6.2%

SEMIO_KULT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.025013998
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:58.561653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.90381623
Coefficient of variation (CV)0.4729961763
Kurtosis-1.05018586
Mean4.025013998
Median Absolute Deviation (MAD)1
Skewness-0.03536077331
Sum3587177
Variance3.624516239
MonotonicityNot monotonic
2021-12-24T14:05:58.672608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3209067
23.5%
5176282
19.8%
1128216
14.4%
7117378
13.2%
4101502
11.4%
6101286
11.4%
257490
 
6.5%
ValueCountFrequency (%)
1128216
14.4%
257490
 
6.5%
3209067
23.5%
4101502
11.4%
5176282
19.8%
6101286
11.4%
7117378
13.2%
ValueCountFrequency (%)
7117378
13.2%
6101286
11.4%
5176282
19.8%
4101502
11.4%
3209067
23.5%
257490
 
6.5%
1128216
14.4%

SEMIO_LUST
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.359086018
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:58.781419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.022829383
Coefficient of variation (CV)0.4640489714
Kurtosis-1.207111475
Mean4.359086018
Median Absolute Deviation (MAD)2
Skewness-0.3030834485
Sum3884909
Variance4.091838712
MonotonicityNot monotonic
2021-12-24T14:05:58.874153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5170040
19.1%
6158624
17.8%
7158234
17.8%
2114373
12.8%
1110382
12.4%
497495
10.9%
382073
9.2%
ValueCountFrequency (%)
1110382
12.4%
2114373
12.8%
382073
9.2%
497495
10.9%
5170040
19.1%
6158624
17.8%
7158234
17.8%
ValueCountFrequency (%)
7158234
17.8%
6158624
17.8%
5170040
19.1%
497495
10.9%
382073
9.2%
2114373
12.8%
1110382
12.4%

SEMIO_MAT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.001596686
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:59.003543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.857540107
Coefficient of variation (CV)0.4641997315
Kurtosis-1.036445759
Mean4.001596686
Median Absolute Deviation (MAD)1
Skewness0.01186754272
Sum3566307
Variance3.45045525
MonotonicityNot monotonic
2021-12-24T14:05:59.104373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5171267
19.2%
4162862
18.3%
2134549
15.1%
3123701
13.9%
7111976
12.6%
197341
10.9%
689525
10.0%
ValueCountFrequency (%)
197341
10.9%
2134549
15.1%
3123701
13.9%
4162862
18.3%
5171267
19.2%
689525
10.0%
7111976
12.6%
ValueCountFrequency (%)
7111976
12.6%
689525
10.0%
5171267
19.2%
4162862
18.3%
3123701
13.9%
2134549
15.1%
197341
10.9%

SEMIO_PFLICHT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.256075654
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:59.227159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.770136694
Coefficient of variation (CV)0.4159081836
Kurtosis-0.8653655223
Mean4.256075654
Median Absolute Deviation (MAD)1
Skewness-0.1694070752
Sum3793104
Variance3.133383917
MonotonicityNot monotonic
2021-12-24T14:05:59.346203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5203845
22.9%
4162117
18.2%
3133990
15.0%
7115458
13.0%
6109442
12.3%
292214
10.3%
174155
 
8.3%
ValueCountFrequency (%)
174155
 
8.3%
292214
10.3%
3133990
15.0%
4162117
18.2%
5203845
22.9%
6109442
12.3%
7115458
13.0%
ValueCountFrequency (%)
7115458
13.0%
6109442
12.3%
5203845
22.9%
4162117
18.2%
3133990
15.0%
292214
10.3%
174155
 
8.3%

SEMIO_RAT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.910139012
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:59.459161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.580305974
Coefficient of variation (CV)0.404155957
Kurtosis-0.3831343065
Mean3.910139012
Median Absolute Deviation (MAD)1
Skewness0.2879717072
Sum3484798
Variance2.497366972
MonotonicityNot monotonic
2021-12-24T14:05:59.550005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4334456
37.5%
2140433
15.8%
3131994
 
14.8%
589056
 
10.0%
787024
 
9.8%
661484
 
6.9%
146774
 
5.2%
ValueCountFrequency (%)
146774
 
5.2%
2140433
15.8%
3131994
 
14.8%
4334456
37.5%
589056
 
10.0%
661484
 
6.9%
787024
 
9.8%
ValueCountFrequency (%)
787024
 
9.8%
661484
 
6.9%
589056
 
10.0%
4334456
37.5%
3131994
 
14.8%
2140433
15.8%
146774
 
5.2%

SEMIO_REL
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.240609232
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:59.671107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.007372556
Coefficient of variation (CV)0.4733689067
Kurtosis-1.134701252
Mean4.240609232
Median Absolute Deviation (MAD)2
Skewness0.00215072809
Sum3779320
Variance4.029544578
MonotonicityNot monotonic
2021-12-24T14:05:59.772016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7211377
23.7%
4207128
23.2%
3150801
16.9%
1108130
12.1%
579566
 
8.9%
273127
 
8.2%
661092
 
6.9%
ValueCountFrequency (%)
1108130
12.1%
273127
 
8.2%
3150801
16.9%
4207128
23.2%
579566
 
8.9%
661092
 
6.9%
7211377
23.7%
ValueCountFrequency (%)
7211377
23.7%
661092
 
6.9%
579566
 
8.9%
4207128
23.2%
3150801
16.9%
273127
 
8.2%
1108130
12.1%

SEMIO_SOZ
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.945859669
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:05:59.902777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.946564233
Coefficient of variation (CV)0.4933181603
Kurtosis-1.353534476
Mean3.945859669
Median Absolute Deviation (MAD)2
Skewness0.1789455842
Sum3516633
Variance3.789112312
MonotonicityNot monotonic
2021-12-24T14:05:59.993586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2244714
27.5%
6136205
15.3%
5121786
13.7%
3118889
13.3%
7117378
13.2%
490161
 
10.1%
162088
 
7.0%
ValueCountFrequency (%)
162088
 
7.0%
2244714
27.5%
3118889
13.3%
490161
 
10.1%
5121786
13.7%
6136205
15.3%
7117378
13.2%
ValueCountFrequency (%)
7117378
13.2%
6136205
15.3%
5121786
13.7%
490161
 
10.1%
3118889
13.3%
2244714
27.5%
162088
 
7.0%

SEMIO_TRADV
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.661784226
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:06:00.124520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.707636767
Coefficient of variation (CV)0.4663400849
Kurtosis-0.655924441
Mean3.661784226
Median Absolute Deviation (MAD)1
Skewness0.3343106362
Sum3263459
Variance2.916023328
MonotonicityNot monotonic
2021-12-24T14:06:00.225092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3226571
25.4%
4174203
19.5%
2132657
14.9%
5117378
13.2%
196775
10.9%
776133
 
8.5%
667504
 
7.6%
ValueCountFrequency (%)
196775
10.9%
2132657
14.9%
3226571
25.4%
4174203
19.5%
5117378
13.2%
667504
 
7.6%
776133
 
8.5%
ValueCountFrequency (%)
776133
 
8.5%
667504
 
7.6%
5117378
13.2%
4174203
19.5%
3226571
25.4%
2132657
14.9%
196775
10.9%

SEMIO_VERT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.023709046
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:06:00.345795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.077746254
Coefficient of variation (CV)0.5163758687
Kurtosis-1.411240267
Mean4.023709046
Median Absolute Deviation (MAD)2
Skewness-0.03560142861
Sum3586014
Variance4.317029496
MonotonicityNot monotonic
2021-12-24T14:06:00.446554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2204333
22.9%
6141714
15.9%
5135205
15.2%
7134756
15.1%
4122982
13.8%
1120437
13.5%
331794
 
3.6%
ValueCountFrequency (%)
1120437
13.5%
2204333
22.9%
331794
 
3.6%
4122982
13.8%
5135205
15.2%
6141714
15.9%
7134756
15.1%
ValueCountFrequency (%)
7134756
15.1%
6141714
15.9%
5135205
15.2%
4122982
13.8%
331794
 
3.6%
2204333
22.9%
1120437
13.5%

SHOPPER_TYP
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
1
254761 
2
207463 
3
190219 
0
127582 
-1
111196 

Length

Max length2
Median length1
Mean length1.124768155
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row3
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1254761
28.6%
2207463
23.3%
3190219
21.3%
0127582
14.3%
-1111196
12.5%

Length

2021-12-24T14:06:00.587828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:06:00.688839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1365957
41.1%
2207463
23.3%
3190219
21.3%
0127582
 
14.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SOHO_KZ
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing73499
Missing (%)8.2%
Memory size6.8 MiB
0.0
810834 
1.0
 
6888

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0810834
91.0%
1.06888
 
0.8%
(Missing)73499
 
8.2%

Length

2021-12-24T14:06:01.556109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:06:01.647104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0810834
99.2%
1.06888
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

STRUKTURTYP
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing97274
Missing (%)10.9%
Memory size6.8 MiB
3.0
555713 
1.0
127607 
2.0
110627 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0555713
62.4%
1.0127607
 
14.3%
2.0110627
 
12.4%
(Missing)97274
 
10.9%

Length

2021-12-24T14:06:01.736171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:06:01.818823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0555713
70.0%
1.0127607
 
16.1%
2.0110627
 
13.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TITEL_KZ
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing73499
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean0.003482846248
Minimum0
Maximum5
Zeros815562
Zeros (%)91.5%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:06:01.899581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08495716307
Coefficient of variation (CV)24.39302714
Kurtosis1998.880458
Mean0.003482846248
Median Absolute Deviation (MAD)0
Skewness39.64777145
Sum2848
Variance0.007217719557
MonotonicityNot monotonic
2021-12-24T14:06:02.020477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0815562
91.5%
11947
 
0.2%
5104
 
< 0.1%
457
 
< 0.1%
349
 
< 0.1%
23
 
< 0.1%
(Missing)73499
 
8.2%
ValueCountFrequency (%)
0815562
91.5%
11947
 
0.2%
23
 
< 0.1%
349
 
< 0.1%
457
 
< 0.1%
5104
 
< 0.1%
ValueCountFrequency (%)
5104
 
< 0.1%
457
 
< 0.1%
349
 
< 0.1%
23
 
< 0.1%
11947
 
0.2%
0815562
91.5%

UMFELD_ALT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing97786
Missing (%)11.0%
Memory size6.8 MiB
4.0
228222 
3.0
208733 
5.0
135160 
2.0
121133 
1.0
100187 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row4.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0228222
25.6%
3.0208733
23.4%
5.0135160
15.2%
2.0121133
13.6%
1.0100187
11.2%
(Missing)97786
11.0%

Length

2021-12-24T14:06:02.171651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:06:02.272531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0228222
28.8%
3.0208733
26.3%
5.0135160
17.0%
2.0121133
15.3%
1.0100187
12.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

UMFELD_JUNG
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing97786
Missing (%)11.0%
Memory size6.8 MiB
5.0
350532 
4.0
225939 
3.0
130403 
2.0
53460 
1.0
 
33101

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row5.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0350532
39.3%
4.0225939
25.4%
3.0130403
 
14.6%
2.053460
 
6.0%
1.033101
 
3.7%
(Missing)97786
 
11.0%

Length

2021-12-24T14:06:02.393516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:06:02.492436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0350532
44.2%
4.0225939
28.5%
3.0130403
 
16.4%
2.053460
 
6.7%
1.033101
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

UNGLEICHENN_FLAG
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing73499
Missing (%)8.2%
Memory size6.8 MiB
0.0
744072 
1.0
 
73650

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0744072
83.5%
1.073650
 
8.3%
(Missing)73499
 
8.2%

Length

2021-12-24T14:06:02.613408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:06:02.716299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0744072
91.0%
1.073650
 
9.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VERDICHTUNGSRAUM
Real number (ℝ≥0)

MISSING
ZEROS

Distinct46
Distinct (%)< 0.1%
Missing97274
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean4.585759503
Minimum0
Maximum45
Zeros368782
Zeros (%)41.4%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:06:02.825092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile25
Maximum45
Range45
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.471520441
Coefficient of variation (CV)1.847353843
Kurtosis6.350593906
Mean4.585759503
Median Absolute Deviation (MAD)1
Skewness2.532641726
Sum3640850
Variance71.76665858
MonotonicityNot monotonic
2021-12-24T14:06:02.968250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0368782
41.4%
1111235
 
12.5%
247613
 
5.3%
329827
 
3.3%
426996
 
3.0%
524019
 
2.7%
621882
 
2.5%
713238
 
1.5%
811864
 
1.3%
1011034
 
1.2%
Other values (36)127457
 
14.3%
(Missing)97274
 
10.9%
ValueCountFrequency (%)
0368782
41.4%
1111235
 
12.5%
247613
 
5.3%
329827
 
3.3%
426996
 
3.0%
524019
 
2.7%
621882
 
2.5%
713238
 
1.5%
811864
 
1.3%
99425
 
1.1%
ValueCountFrequency (%)
451165
0.1%
441435
0.2%
431321
0.1%
421324
0.1%
411329
0.1%
401359
0.2%
391660
0.2%
381622
0.2%
371348
0.2%
361959
0.2%

VERS_TYP
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
2
398722 
1
381303 
-1
111196 

Length

Max length2
Median length1
Mean length1.124768155
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2398722
44.7%
1381303
42.8%
-1111196
 
12.5%

Length

2021-12-24T14:06:03.109787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:06:03.200598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1492499
55.3%
2398722
44.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VHA
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing73499
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean0.4388202837
Minimum0
Maximum5
Zeros665547
Zeros (%)74.7%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:06:03.281118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.143290454
Coefficient of variation (CV)2.605372853
Kurtosis7.390683117
Mean0.4388202837
Median Absolute Deviation (MAD)0
Skewness2.889777008
Sum358833
Variance1.307113063
MonotonicityNot monotonic
2021-12-24T14:06:03.412382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0665547
74.7%
181016
 
9.1%
424469
 
2.7%
522372
 
2.5%
319445
 
2.2%
24873
 
0.5%
(Missing)73499
 
8.2%
ValueCountFrequency (%)
0665547
74.7%
181016
 
9.1%
24873
 
0.5%
319445
 
2.2%
424469
 
2.7%
522372
 
2.5%
ValueCountFrequency (%)
522372
 
2.5%
424469
 
2.7%
319445
 
2.2%
24873
 
0.5%
181016
 
9.1%
0665547
74.7%

VHN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing121196
Missing (%)13.6%
Memory size6.8 MiB
2.0
233844 
3.0
179579 
4.0
178413 
1.0
141321 
0.0
36868 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row0.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0233844
26.2%
3.0179579
20.1%
4.0178413
20.0%
1.0141321
15.9%
0.036868
 
4.1%
(Missing)121196
13.6%

Length

2021-12-24T14:06:03.553629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:06:03.654467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0233844
30.4%
3.0179579
23.3%
4.0178413
23.2%
1.0141321
18.4%
0.036868
 
4.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VK_DHT4A
Real number (ℝ≥0)

MISSING

Distinct11
Distinct (%)< 0.1%
Missing75917
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean6.001214271
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:06:03.765030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.856091359
Coefficient of variation (CV)0.4759189108
Kurtosis-1.205256256
Mean6.001214271
Median Absolute Deviation (MAD)3
Skewness-0.158692708
Sum4892814
Variance8.157257851
MonotonicityNot monotonic
2021-12-24T14:06:03.875709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10114500
12.8%
796693
10.8%
988238
9.9%
886090
9.7%
382570
9.3%
680078
9.0%
274200
8.3%
570671
7.9%
470198
7.9%
149535
5.6%
(Missing)75917
8.5%
ValueCountFrequency (%)
149535
5.6%
274200
8.3%
382570
9.3%
470198
7.9%
570671
7.9%
680078
9.0%
796693
10.8%
886090
9.7%
988238
9.9%
10114500
12.8%
ValueCountFrequency (%)
112531
 
0.3%
10114500
12.8%
988238
9.9%
886090
9.7%
796693
10.8%
680078
9.0%
570671
7.9%
470198
7.9%
382570
9.3%
274200
8.3%

VK_DISTANZ
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)< 0.1%
Missing75917
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean7.532130346
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:06:03.996423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median8
Q310
95-th percentile12
Maximum13
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.247788909
Coefficient of variation (CV)0.4311912778
Kurtosis-0.7823473088
Mean7.532130346
Median Absolute Deviation (MAD)2
Skewness-0.3536317455
Sum6140976
Variance10.5481328
MonotonicityNot monotonic
2021-12-24T14:06:04.127122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1094320
10.6%
892104
10.3%
989067
10.0%
784222
9.5%
683291
9.3%
1179990
9.0%
371410
8.0%
1259994
6.7%
144858
5.0%
538791
4.4%
Other values (3)77257
8.7%
(Missing)75917
8.5%
ValueCountFrequency (%)
144858
5.0%
219917
 
2.2%
371410
8.0%
429786
 
3.3%
538791
4.4%
683291
9.3%
784222
9.5%
892104
10.3%
989067
10.0%
1094320
10.6%
ValueCountFrequency (%)
1327554
 
3.1%
1259994
6.7%
1179990
9.0%
1094320
10.6%
989067
10.0%
892104
10.3%
784222
9.5%
683291
9.3%
538791
4.4%
429786
 
3.3%

VK_ZG11
Real number (ℝ≥0)

MISSING

Distinct11
Distinct (%)< 0.1%
Missing75917
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean5.9459723
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:06:04.235794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.771463653
Coefficient of variation (CV)0.4661077304
Kurtosis-1.071798028
Mean5.9459723
Median Absolute Deviation (MAD)2
Skewness-0.09933345399
Sum4847775
Variance7.681010782
MonotonicityNot monotonic
2021-12-24T14:06:04.408986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1097938
11.0%
597777
11.0%
688581
9.9%
788552
9.9%
486600
9.7%
883994
9.4%
982134
9.2%
369634
7.8%
259916
6.7%
152009
5.8%
(Missing)75917
8.5%
ValueCountFrequency (%)
152009
5.8%
259916
6.7%
369634
7.8%
486600
9.7%
597777
11.0%
688581
9.9%
788552
9.9%
883994
9.4%
982134
9.2%
1097938
11.0%
ValueCountFrequency (%)
118169
 
0.9%
1097938
11.0%
982134
9.2%
883994
9.4%
788552
9.9%
688581
9.9%
597777
11.0%
486600
9.7%
369634
7.8%
259916
6.7%

W_KEIT_KIND_HH
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing107602
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean3.933406413
Minimum0
Maximum6
Zeros40386
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:06:04.580179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.964701066
Coefficient of variation (CV)0.4994909908
Kurtosis-1.086501848
Mean3.933406413
Median Absolute Deviation (MAD)2
Skewness-0.4529771657
Sum3082292
Variance3.86005028
MonotonicityNot monotonic
2021-12-24T14:06:04.688840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6281966
31.6%
4128675
14.4%
3100170
 
11.2%
284000
 
9.4%
183706
 
9.4%
564716
 
7.3%
040386
 
4.5%
(Missing)107602
 
12.1%
ValueCountFrequency (%)
040386
 
4.5%
183706
 
9.4%
284000
 
9.4%
3100170
 
11.2%
4128675
14.4%
564716
 
7.3%
6281966
31.6%
ValueCountFrequency (%)
6281966
31.6%
564716
 
7.3%
4128675
14.4%
3100170
 
11.2%
284000
 
9.4%
183706
 
9.4%
040386
 
4.5%

WOHNDAUER_2008
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing73499
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean7.908790518
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:06:04.811645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median9
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.92313705
Coefficient of variation (CV)0.2431644947
Kurtosis1.194461486
Mean7.908790518
Median Absolute Deviation (MAD)0
Skewness-1.620093885
Sum6467192
Variance3.698456114
MonotonicityNot monotonic
2021-12-24T14:06:04.932395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9551176
61.8%
880118
 
9.0%
450736
 
5.7%
338767
 
4.3%
635170
 
3.9%
530959
 
3.5%
723939
 
2.7%
26174
 
0.7%
1683
 
0.1%
(Missing)73499
 
8.2%
ValueCountFrequency (%)
1683
 
0.1%
26174
 
0.7%
338767
 
4.3%
450736
 
5.7%
530959
 
3.5%
635170
 
3.9%
723939
 
2.7%
880118
 
9.0%
9551176
61.8%
ValueCountFrequency (%)
9551176
61.8%
880118
 
9.0%
723939
 
2.7%
635170
 
3.9%
530959
 
3.5%
450736
 
5.7%
338767
 
4.3%
26174
 
0.7%
1683
 
0.1%

WOHNLAGE
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)< 0.1%
Missing93148
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean4.052836019
Minimum0
Maximum8
Zeros6950
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:06:05.073179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q35
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.949538668
Coefficient of variation (CV)0.4810307299
Kurtosis-0.8440882651
Mean4.052836019
Median Absolute Deviation (MAD)1
Skewness0.4396769473
Sum3234459
Variance3.800701019
MonotonicityNot monotonic
2021-12-24T14:06:05.191787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3249719
28.0%
7169318
19.0%
4135973
15.3%
2100376
11.3%
574346
 
8.3%
143918
 
4.9%
817473
 
2.0%
06950
 
0.8%
(Missing)93148
 
10.5%
ValueCountFrequency (%)
06950
 
0.8%
143918
 
4.9%
2100376
11.3%
3249719
28.0%
4135973
15.3%
574346
 
8.3%
7169318
19.0%
817473
 
2.0%
ValueCountFrequency (%)
817473
 
2.0%
7169318
19.0%
574346
 
8.3%
4135973
15.3%
3249719
28.0%
2100376
11.3%
143918
 
4.9%
06950
 
0.8%

ZABEOTYP
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3624376
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 MiB
2021-12-24T14:06:05.314668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.352704299
Coefficient of variation (CV)0.4022987071
Kurtosis-0.2449129835
Mean3.3624376
Median Absolute Deviation (MAD)1
Skewness0.02846326419
Sum2996675
Variance1.82980892
MonotonicityNot monotonic
2021-12-24T14:06:05.433479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3364905
40.9%
4210095
23.6%
1123622
 
13.9%
584956
 
9.5%
674473
 
8.4%
233170
 
3.7%
ValueCountFrequency (%)
1123622
 
13.9%
233170
 
3.7%
3364905
40.9%
4210095
23.6%
584956
 
9.5%
674473
 
8.4%
ValueCountFrequency (%)
674473
 
8.4%
584956
 
9.5%
4210095
23.6%
3364905
40.9%
233170
 
3.7%
1123622
 
13.9%

ANREDE_KZ
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
2
465305 
1
425916 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2465305
52.2%
1425916
47.8%

Length

2021-12-24T14:06:05.576438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:06:05.677021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2465305
52.2%
1425916
47.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
3
358533 
4
228510 
2
158410 
1
142887 
9
 
2881

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3358533
40.2%
4228510
25.6%
2158410
17.8%
1142887
 
16.0%
92881
 
0.3%

Length

2021-12-24T14:06:05.747488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-24T14:06:05.838067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3358533
40.2%
4228510
25.6%
2158410
17.8%
1142887
 
16.0%
92881
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sample

First rows

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